AgriEngineering doi: 10.3390/agriengineering8060200

Authors: Baltasar Miras-Cabrera Adela Ramos-Escudero Carlos Toledo Javier Padilla

The rapid expansion of solar photovoltaics is intensifying competition for land and highlighting the need for scalable energy solutions that can be integrated into existing power systems without displacing agricultural activity. Once the technical and agronomic viability of agrivoltaic configurations has been demonstrated at field scale, a critical next step toward their market consolidation is the assessment of their deployment potential at regional scales from an energy systems and grid integration perspective. This study presents a GIS-based framework to evaluate the large-scale implementation of vertically integrated agrivoltaic systems, using vineyard landscapes in the Region of Murcia (southeastern Spain) as a representative case study. The analysis combines high-resolution land-use data, crop distribution, regulatory constraints on grid connection distances, and existing electrical infrastructure to quantify installable capacity, energy production, self-consumption potential, and grid accessibility. Results indicate that vertically mounted bifacial PV systems could reach up to 7.06 GWp, generating approximately 11.84 TWh/year, while revealing a pronounced spatial mismatch between optimal agrivoltaic production sites and current grid connection points. This distance-dependent distribution highlights the need for differentiated deployment strategies, balancing local self-consumption, grid reinforcement, and centralized injection. Beyond the specific case examined, the proposed approach provides a transferable framework for energy system planning, supporting grid-aware agrivoltaic deployment in diverse regions and regulatory contexts.

AgriEngineering doi: 10.3390/agriengineering8050199

Authors: Samal Abayeva Sana Kabdrakhmanova

This scoping review maps 224 empirical studies (205 from a structured Scopus search, 2020–2026, plus 19 from a targeted Central Asia supplement) across four digital technology domains for crop production: IoT and sensor-based systems, UAVs and remote sensing, machine learning and AI, and nanostructured agrochemicals. The review follows the PRISMA-ScR framework and pursues three research questions concerning documented effects and validation limitations (RQ1); cross-cutting barriers in human capital, data governance, and infrastructure (RQ2); and the state of empirical evidence from Central Asia and Kazakhstan relative to international findings (RQ3). Across all four domains, the strongest reported effects occur where the data-to-decision-to-action loop is closed and sustained over multiple seasons, yet most published metrics rest on single-season, single-site, or controlled-environment validation that overstates likely field portability. IoT and selected UAV and ML workflows are closest to operational readiness where maintenance, calibration, and advisory support are sustained. Nanostructured materials remain the least mature domain in agronomic terms. For Central Asia, foundational monitoring and salinity-oriented remote sensing are the most immediately transferable elements; intervention-grade ML and integrated digital systems require local calibration, extension infrastructure, and multi-season field validation that are largely still absent. The review identifies the digital skills gap, incomplete data governance, and underreported total cost of ownership as the principal institutional barriers to scaling. Policy priorities include shifting from technical pilots to multi-season agronomic proof, building intermediary service capacity, and establishing transparent data-governance frameworks before large-scale procurement.

AgriEngineering doi: 10.3390/agriengineering8050198

Authors: Li Shi Wang Yue Sun Zhuo Tan

Accurate instance segmentation of oranges in complex orchard environments is crucial for obtaining clean regions of interest (ROIs). Coarse region extraction may include non-target pixels from leaves, shadows, background, and adjacent fruits, thereby increasing boundary pixel mixing in subsequent hyperspectral-assisted observation. This study proposes an improved lightweight YOLO11n-Seg method as an RGB-based visual front-end for cleaner single-fruit ROI extraction. Its contribution lies in the task-oriented integration of three complementary components: a Local Deformable Convolution Backbone (LDC-Backbone) for representing irregular and occluded fruit contours, a Boundary-Guided GSConv (BG-GSConv) module for efficiently fusing shallow boundary details with deep semantic features, and an ROI-Purity-Oriented Dice Boundary Loss for constraining mask integrity and boundary adherence. Evaluated on a complex orchard dataset, the improved model achieved a Mask mAP@0.5 of 0.962, a Mask mAP@0.5:0.95 of 0.692, a Box mAP@0.5 of 0.942, and an inference speed of 101 FPS with 3.20 M parameters. Background leakage analysis further showed that the proposed model reduced the inclusion of non-fruit pixels in extracted ROIs, supporting cleaner mask-based single-fruit region extraction. Preliminary ROI-based reflectance observation indicated that the reflectance curves obtained from the improved-model ROIs were closer to those of manually referenced pure ROIs than those obtained from the baseline extraction. These results suggest that the proposed method can serve as a real-time RGB-based front-end for cleaner single-fruit ROI extraction and later hyperspectral-assisted sampling. Complete closed-loop spectral quality modeling with paired RGB–HSI data remains a direction for future work.

AgriEngineering doi: 10.3390/agriengineering8050197

Authors: Waqas Nawaz Sikandar Ahmad Pezzuolo

Agricultural pesticide wastewater represents a significant environmental and public health challenge, highlighting the need for scalable and resource-efficient treatment strategies. This review adopted a PRISMA-based methodology using the Scopus and Web of Science databases, leading to the analysis of 176 peer-reviewed studies published between 2014 and 2025. The selected literature was critically examined to assess pesticide wastewater treatment technologies, including adsorption, membrane filtration (MF), advanced oxidation processes (AOPs), biological treatments, and hybrid configurations. Particular attention was given to their treatment performance, scalability from farm to district level, resource recovery potential, economic feasibility, and life-cycle assessment (LCA) implications. Among the evaluated systems, hybrid configurations combining biological processes with AOPs or MF generally showed higher removal performance, often achieving more than 80% pesticide residue removal, while offering greater adaptability and compatibility with circular biorefinery frameworks. The review identifies key opportunities for resource recovery, including methane and hydrogen production, nutrient recycling, water reuse, and chemical reclamation, thereby supporting circular bioeconomy objectives. Overall, this review proposes an integrated, multiscale circular biorefinery perspective for sustainable pesticide wastewater management and identifies research priorities for developing resilient, safe, and resource-efficient agricultural water treatment systems.

AgriEngineering doi: 10.3390/agriengineering8050196

Authors: Megan A. Wehrbein Federico Podversich Hector M. Menendez Zachary K. F. Smith Warren C. Rusche Ana Clara B. Menezes

Winter feeding represents a significant cost in beef production, requiring efficient strategies that maintain productivity while minimizing environmental impact. Forty-six pregnant cows (620 ± 61 kg BW) were used to evaluate an ad libitum hay-based diet (2.02 Mcal/kg ME; HFOR; n = 23) versus a corn-based diet (2.84 Mcal/kg ME) limit-fed at 1.2% BW (HCON; n = 23) from 50 d pre-calving to 84 d post-calving. Pre- and post-calving, HCON cows consumed less (p < 0.01) dry matter, crude protein, and water than HFOR cows. While CH4 yield per kg DMI was greater (p < 0.01) for HCON cows, total daily CH4 emissions and CH4 per unit of NEm intake were lower (p ≤ 0.03) compared with HFOR cows. Behavioral data showed that HCON cows had fewer (p < 0.01) meals and spent less time eating, but had greater intake per minute. Cow BW differed by treatment over time (p < 0.01), with HCON cows weighing less through early lactation, though no differences were observed from d 84 to weaning. Calf BW remained unaffected (p ≥ 0.76). In conclusion, limit-feeding a corn-based diet improves feed and water use efficiency and reduces enteric CH4 emissions without compromising calf growth, offering a viable alternative to traditional forage-based wintering systems.

AgriEngineering doi: 10.3390/agriengineering8050195

Authors: Sanganamoni Shivashankar Musunuru Venkata Prasad Kancherla Suresh Ravindra Naik Kesana Manikanta

Ablation and harvesting are among the most labor-intensive and physically demanding operations in oil palm cultivation, often resulting in significant drudgery and safety concerns when performed manually through climbing or pole-assisted methods. To overcome these challenges, a motorized backpack-type machine was developed and evaluated for its field performance, ergonomics, and economic feasibility. The machine met required quality standards and exhibited satisfactory performance under field conditions, achieving average ablation and harvesting capacities of 286 inflorescences per day and 4.115 t day−1, with actual field capacities of 0.727 ha h−1 (ablation), 0.516 ha h−1 (sickle), and 0.537 ha h−1 (chisel), and field efficiencies of 81.23%, 76.3%, and 79.91%, respectively. Ergonomic evaluation indicated that operation of the machine falls within a moderate workload category, thereby reducing operator fatigue compared to manual methods. Economic analysis further revealed that the cost of operation was substantially reduced to 3.02 USD t−1 and 60.40 USD ha−1 year−1, resulting in increased harvester earnings of 174.72% and 64.83% compared to climbing and pole harvesting methods, respectively. These findings demonstrate that the motorized backpack machine is a practical, efficient, and economically viable alternative to traditional techniques and minimizes drudgery while improving productivity and profitability in oil palm plantations.

AgriEngineering doi: 10.3390/agriengineering8050194

Authors: Milica Aćimović Biljana Lončar Olja Šovljanski Ana Tomić Vanja Travičić Milada Pezo Vladimir Filipović Danijela Šuput Darko Micić Lato Pezo

The geographical origin of aniseed (Pimpinella anisum L.) represents a key quality determinant, as it directly influences the chemical composition and commercial value of its essential oil. Agronomic traits of aniseed (plant height, umbel diameter, number of umbels per plant), productivity-related traits (number of seeds, thousand-seed weight, yield per plant, plant biomass, harvest index, yield per hectare, essential oil content and yield), and physiological traits (germination energy and total germination) exhibit variations depending on geographical origin. The study proposes an integrated framework for accurate classification by combining agronomic, productivity, and physiological data with GC-MS profiles and advanced machine learning (ML) techniques. A total of 144 samples were analyzed, based on a factorial design including three locations, six fertilizer treatments, two years, and four replications. trans-Anethole was the dominant compound in all samples (89.508–101.441%). Several classification models, including artificial neural networks, random forests, MARSplines, boosted trees, interactive trees, naïve Bayes, and support vector machines, were evaluated to discriminate samples by geographical origin using agro-meteorological and GC-MS data. The results indicate that AI and ML approaches effectively captured complex non-linear relationships. Overall, the multi-model framework highlights the strong potential of machine learning for agro-food authentication, supporting improved traceability, site-specific decision-making, and quality control.

AgriEngineering doi: 10.3390/agriengineering8050193

Authors: Wannaporn Thepbandit Daniel Martinez Lacasa Wilawan Chuaboon Dusit Athinuwat

This study developed and evaluated a cloud-based smart irrigation platform (DSmart Farming) integrating low-cost sensors and IoT technology for automated irrigation control in community greenhouses of Puen Jai Insee, organic group in Sa Kaeo Province. The system combined soil moisture, air temperature, and relative humidity sensors, with a LoRa32-based control unit in each greenhouse and a central web-based management application linked to a MariaDB database on a cloud server. Five vegetable crops, including cherry tomato, broccoli, cabbage, Chinese kale, and kale, were grown over two distinct seasons under four irrigation strategies in a completely randomized design with three replications: three smart irrigation treatments based on soil moisture thresholds (on/off at 40/50%, 45/55%, and 50/60%) and a farmer-managed conventional irrigation control. The smart irrigation system maintained root-zone moisture within the target range (approximately 50–60%) and moderated greenhouse microclimate, preventing daytime temperatures from exceeding 40 °C, in contrast to 40–45 °C peaks in the conventional greenhouses. Across crops, smart irrigation increased yields by 20–29% while reducing water use by 41–60% compared to conventional practice, leading to income increases of 20–56%, depending on the crop. Bacterial soft rot caused by Pectobacterium carotovorum subsp. carotovorum occurred only under conventional irrigation, whereas no soft rot or other major diseases were detected in smart-irrigated greenhouses. These results demonstrate that the DSmart Farming system can enhance water use efficiency, avoid disease incidence, and improve the productivity and profitability of organic greenhouse vegetable production in water-limited smallholder systems.

AgriEngineering doi: 10.3390/agriengineering8050192

Authors: Emerson Cristi de Barros Gefferson Pereira da Paixão José Augusto Amorim Silva do Sacramento Paulo Sérgio Taube João Thiago Rodrigues de Sousa

Climate change poses a significant challenge to food security, as it alters crop productivity, distribution patterns, and the overall food supply. This study modelled the emergence of Amaranthus hybridus L. in bean (Phaseolus vulgaris L.) and maize (Zea mays L.) production systems in the Brazilian state of Minas Gerais, in the cities of Coimbra, Paracatu, São João del-Rei, and Uberaba, under the Coupled Model Intercomparison Project Phase 6 (CMIP6) SSP1-2.6 and SSP5-8.5 scenarios. Using Hydrothermal Time (HTT), computational modelling, and nonlinear Weibull regression, weed emergence was simulated under current and future climate scenarios for 2050 and 2070. Although biological triggers such as temperature and base water potential remain constant, higher average temperatures accelerate HTT accumulation. Thus, this results in earlier and more intense emergence flows. The highest and lowest cumulative emergence were observed in Uberaba and Paracatu, respectively. The SSP5-8.5 scenario projects high emergence windows for 2070. This reduces the time available for management interventions. The root-mean-square error (RMSE) associated with the coefficient of determination (R2) of the models validates HTT as an essential tool in computational agriculture. The integration of these models into decision-support systems is essential to mitigating productivity losses and it will increase control efficiency amid future climate uncertainties.

AgriEngineering doi: 10.3390/agriengineering8050191

Authors: Kittikun Pituprompan Teerasak Malasri Nattapong Miyapan Onnicha Khainunlai Vitsanusat Atyotha

Agricultural soils are a major source of greenhouse gas (GHG) emissions, particularly carbon dioxide (CO2) and methane (CH4), highlighting the need for cost-effective and field-applicable monitoring solutions. This study developed and evaluated a low-cost real-time monitoring system for soil CO2 and CH4 emissions by integrating surface emission chambers, low-cost gas sensors, a solar-powered energy supply, and IoT-based wireless communication. Three acrylic chambers with different heights (40, 60, and 80 cm) were fabricated to investigate the influence of chamber geometry on measurement performance. System performance was assessed through simultaneous measurements against a Biogas 5000 analyzer under simulated conditions and during field deployment in a sugarcane cultivation area in Khon Kaen Province, Thailand. Relative agreement was used to compare the developed system with the reference instrument. The results showed that relative agreement varied with chamber height for both gases. Under simulated conditions, the 80 cm chamber achieved the highest overall relative agreement for CO2 and CH4, underscoring the importance of sufficient headspace volume in chamber-based measurements. Field experiments confirmed the system’s capability for continuous CO2 monitoring in an agricultural environment. However, CH4 emissions were not detected during the study period, likely due to drought-induced, well-aerated soil conditions. The developed system demonstrated stable autonomous operation, low energy consumption, and ease of installation, making it suitable for long-term field applications. Overall, the proposed platform provides a practical and scalable approach for real-time soil GHG monitoring and offers strong potential for integration into precision agriculture and climate-smart farming systems to support GHG mitigation strategies.

AgriEngineering doi: 10.3390/agriengineering8050190

Authors: Alejandro Cruz-Escabias Jesús Montes-Romero João Gabriel Bessa Pedro J. Pérez-Higueras Eduardo F. Fernández Florencia Almonacid

Integrating semi-transparent photovoltaics (STPVs) into greenhouses offers a dual-use solution for land efficiency, although matching electricity generation with crop spectral needs remains a challenge. To address this, this study assesses the optical and microclimatic impact of Cadmium Telluride (CdTe, 50% transparency) and amorphous Silicon (a-Si, 20%) technologies compared to a conventional control in a semi-arid Mediterranean climate. Spectral analysis revealed that CdTe aligned with chlorophyll absorption peaks, preserving a transparency window that yielded a 66% relative gain in biologically useful radiation over the blue-blocking a-Si. Furthermore, while both technologies significantly reduced Photosynthetically Active Radiation (PAR), this shading served as a protective filter against supra-optimal irradiance, stabilizing the internal microclimate. In the control prototype, extreme vapour pressure deficits (VPDs approaching 9.0 kPa) drove maximum reference evapotranspiration (ET0) above 4.6 mm/day. In contrast, the STPV systems effectively capped ET0 at approximately 3.09 mm/day (CdTe) and 1.64 mm/day (a-Si) through their radiative attenuation, despite internal VPDs still reaching 6.5–7.0 kPa during peak summer. This decoupling resulted in drastic average ET0 reductions of 31.4% and 61.3%, respectively, while mitigating soil overheating by up to 17.8%. These findings demonstrate that specific STPV technologies transcend mere shading to function as passive climate resilience tools, naturally enforcing water conservation and physically disarming atmospheric aridity in high-radiation environments.

AgriEngineering doi: 10.3390/agriengineering8050189

Authors: Karen Margarita Viloria-Benítez Claudia Denise De Paula Ricardo David Andrade-Pizarro Mónica María Simanca-Sotelo Alba Manuela Durango-Villadiego José Antonio Rubio-Arrieta

The drying kinetics of three varieties of cassava were evaluated in a tray dryer, using a completely randomized design with a three-factor factorial arrangement: temperature (50, 60, and 70 °C), air velocity (1, 2, and 3 m/s), and variety (“Blanca Mona”, “Ica Negrita”, “Venezolana”), with three replicates per treatment. The results obtained were used to construct drying curves, which showed that this process occurred in the decreasing period. The drying curves were adjusted to mathematical models, and the Page model was the best fit to the experimental data with R2adj values closer to 1 and RSS values less than 0.0086. The effective diffusivities (Deff) in cassava flours were represented by the Arrhenius equation with values ranging from 5.24 × 10−10 to 1.58 × 10−9 m2/s. The activation energy (Ea) recorded values between 20.34 and 28.32 kJ/mol. The flours from the three cassava varieties were obtained under the best drying conditions (70 °C and 3 m/s). The physicochemical characterization of fresh roots and flours from three cassava varieties revealed significant genotype-dependent differences in their proximal composition. Blanca Mona exhibited the highest ash content and the lowest total carbohydrates among fresh roots, while Ica Negrita stood out for its superior crude fiber content in flour. Venezolana flour stood out for its higher protein content (3.86 ± 0.04 g/100 g) and significant fiber content (1.39 ± 0.39 g/100 g), making it the flour with the best nutritional profile and greatest potential for food applications. Therefore, tray drying is recommended as one of the suitable methods for cassava flour production.

AgriEngineering doi: 10.3390/agriengineering8050188

Authors: Dongdong Gu Yuhan Wang Yang Wang Botao Zhu Jie Yang Jianqun Jing

China boasts abundant straw resources but grapples with notable challenges in straw processing: returning straw to fields can lead to soil compaction and aggravated pests/diseases, while baled straw for off-field storage and transportation tends to scatter. Additionally, domestic netting technology for square bales remains underdeveloped, and imported equipment is ill-suited for small-scale farmers. To tackle these issues, this study developed an integrated straw baling and netting machine by modifying the 9YFSG-2.2 square straw baler. It integrates a conveying mechanism, an offset crank–connecting rod compression mechanism (300 mm crank, 885 mm connecting rod), a two-stage gear-driven net-wrapping mechanism (with hollowed-out large gears for weight reduction), and a sensor-controlled net-cutting device, forming a complete workflow of “straw pick-up–shredding–conveying–compaction–net wrapping–net cutting”. Via coupled simulation using RecurDyn 2019, EDEM 2020, and ANSYS Workbench 2018, straw particles were modeled as 28-mm-long segments (composed of three 7 mm spheres). Simulations showed straw compaction in 0.48 s, with the compression chamber and plate having equivalent stresses of 0.2767 MPa and 173.44 MPa and maximum deformations of 0.0012 mm and 0.66 mm—both well below structural steel’s yield strength. Field tests in Xinxiang, Henan (straw moisture 30.03%), yielded results exceeding standards: 99.4% bale formation rate, 96% regular bale rate, 93% drop resistance rate, 170 kg/m3 bale density, and 12 s per bale efficiency. Controlling netting time further boosted efficiency and reduced consumption, successfully realizing integrated straw baling and netting.

AgriEngineering doi: 10.3390/agriengineering8050187

Authors: Yujun Wang Yating Zhan Ke Song Yin Li Ziqiao Xu Hui Mu Yingshi Xu Yanmei Cui Liang Hang

Precise mapping of rice cultivars is of great significance for crop management and food security evaluation. Nevertheless, differentiating between Indica and Japonica rice remains a formidable task, mainly due to subtle discrepancies in spectral characteristics and scattered planting distributions. This study evaluated the synergistic effect of spatial resolution and red edge information in rice variety classification using PlanetScope (PS) and Sentinel-2 (S2) images from the Tillering and Jointing stage, Heading and Flowering stage in Huai’an, Jiangsu Province. Multiple feature schemes were constructed, including spectral bands, vegetation indices, and texture features, with and without red-edge variables. A total of eight feature schemes have been constructed, including spectral bands, vegetation index, texture features, and red edge features. The feature scheme division is based on the participation of different sensors, growth periods, and red edges. We fine-tune three classification models, Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and TabNet, to enhance classification performance. Additionally, we employ Shapley Additive Explanations (SHAP) to quantitatively measure the contribution of each feature to the prediction of distinct rice varieties. Results demonstrate that classification accuracy of different sensors reach the highest at the Heading and Flowering stage. The overall accuracy of PS scheme is 98.14%, the F1 scores of Japonica and Indica rice are 97.67% and 98.41%, the overall accuracy of S2 scheme is 97.87%, and the F1 scores of Japonica and Indica rice are 98.62% and 98.68, respectively. Incorporating red-edge features leads to a notable improvement in F1-scores for both Indica and Japonica rice under all experimental configurations. Although PS only has one red edge band set, its classification performance is similar to S2, and the boundaries between different rice variety recognition results and between non rice and rice plots are more refined compared to S2. Feature attribution analysis reveals that red-edge indices exert a dominant influence on the decision-making process of the models, especially during the Heading–Flowering period. These findings suggest that high-accuracy discrimination of rice varieties relies heavily on the synergistic optimization of phenological timing, red-edge spectral information, and spatial resolution, rather than merely increasing spectral dimensionality. The optimization direction for high-precision rice variety mapping in the future should prioritize the collaborative mechanism of phenological period, red edge data, and spatial resolution, rather than being limited to simple stacking in the spectral dimension.

AgriEngineering doi: 10.3390/agriengineering8050186

Authors: Kevin J. Shinners Brian M. Huenink Walter M. Schlesser Jacob R. Flick Matthew F. Digman

Yield monitoring in forage production is typically limited to chopping or baling operations, where spatial resolution is often reduced by windrow merging. This study evaluated the feasibility of estimating mass flow rate (MFR) and generating spatial yield maps at the mowing stage using sensors integrated into a windrower. Conditioning roll speed, swath shield impact force, and the displacement of spring-loaded vanes (fingers) in the crop flow were evaluated during alfalfa harvest and calibrated against measured MFR. Model performance was assessed using cross-validation, and spatial fidelity was evaluated using experimental variograms and kriged yield maps. The average MFR was 19 kg·s−1 with a range of 4 to 55 kg·s−1. Conditioning roll speed provided the most robust and transferable predictor of MFR (R2 = 0.89, RMSE = 3.4 kg·s−1), consistently outperforming impact force (R2 = 0.70, RMSE = 1.9 kg·s−1) and finger displacement (R2 = 0.82, RMSE = 4.3 kg·s−1), which were more sensitive to machine dynamics and sensor placement. Validation of the roll-speed model using an independent dataset resulted in an R2 = 0.87 and RMSE of 2.62 kg·s−1. Yield maps derived from roll-speed-based models exhibited clear spatial structure with correlation lengths of approximately 25–40 m, whereas the finger displacement model exhibited higher nugget effects. Yield mapping with the forage harvester showed reduced spatial fidelity compared to mowing stage estimates, as windrow merging prior to chopping caused spatial averaging that diminished recoverable fine-scale yield variability. These results demonstrate that yield monitoring at the mowing stage enabled yield estimates to complement downstream harvest data and improve characterization of within-field yield variability.

AgriEngineering doi: 10.3390/agriengineering8050185

Authors: Nelundeniyage Sumuduni L. Senevirathne Parwit Chutichaimaytar Tofael Ahamed

The analysis of soil carbon helps various sectors, including agriculture, in the context of monitoring soil health. In precision agriculture, decisions are made on the basis of site-specific information and thus have the potential to increase crop productivity more than is possible with traditional high-input agriculture. Site-specific information-based nutrition management, pest and disease management, and water management are the main areas of interest in the era of precision agriculture. Soil organic carbon (SOC) is one of the main components of the carbon cycle and impacts soil physical and chemical properties. Soil color is considered an indicator of soil carbon. In relation to soil physical properties, soil color has been used to determine SOC level and classification throughout history in a qualitative manner, and recently, researchers have shown interest in relating soil color data to quantify soil chemical properties. From spectroscopy-based color analysis to image-based color analysis, research has shown strong relationships between SOC and color properties. Therefore, with the improvement of technology to create smaller and portable sensors, the potential exists to automate the processes of soil chemical analysis to use them in precision agriculture. Two of the major limitations of these methodologies in research are the number of known soil samples required to calibrate a model (the majority of the models require more than 100 samples) and the use of expensive spectrometers with complex processes. Thus, the potential of individual farmers to deploy these methods is limited. This research was conducted to develop a methodology with complete guidelines and a set of tools to allow farmers to analyze SOC themselves. Furthermore, by encouraging farmers to analyze their farmland soils for SOC and update the data, the research enables them to potentially use this information to manage their agronomic practices, including the addition of organic fertilizer to reduce soil carbon pool inefficiencies and decisions regarding the mode of tillage and water management. During this research, three sensors and different combinations of sensors were used to capture soil surface color, temperature, and reflectance and were considered for model development. The highest-model-fit equation was obtained from the thermal image and red, green, and blue (RGB) image combinations (R2 = 0.65 and MSE = 0.0335). The variables used for X from the color models were hue values and redness (a), and those from the thermal image minimum and maximum temperature data were used. Finally, using a regression equation along with the image data and SOC data from the chemical analysis, a farmer-feedback-based SOC prediction model was developed.

AgriEngineering doi: 10.3390/agriengineering8050184

Authors: Éva Horváth Péter Zagyi Péter Fejér Tamás Rátonyi László Duzs Balázs Csizi Adrienn Széles

Population growth, climate change, and increasing pressure on water and nitrogen resources pose major challenges for sustainable maize production. Maize yield is highly sensitive to inter-annual weather variability, yet many prediction approaches still rely on simple linear relationships and rarely integrate SPAD (Soil Plant Analysis Development)-based crop diagnostics with machine learning in multi-year nitrogen × irrigation experiments. In a three-year field experiment (2018–2020) in Hungary, we evaluated how basal and top-dressing fertilization and supplemental irrigation under contrasting water supply conditions affected the chlorophyll status and grain yield of a maize hybrid. Relative chlorophyll content was monitored using SPAD measurements at key phenological stages (V6, V12, and R1), and a multilayer perceptron (MLP) model was developed to improve yield prediction and to identify informative combinations of input variables. Five alternative scenarios (SC1–SC5) were tested by combining SPAD values with the fertilization rate, irrigation status, and crop year in different configurations, and model performance was assessed using root mean square deviation (RMSD), mean absolute error (MAE), normalized root mean square error (NRMSE), correlation (r, r2), Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE), Kendall’s tau, and the index of agreement (d). Overall, SC4 (SPAD + fertilization + crop year + irrigation) achieved the best agreement with observed yields across most indices (e.g., r ≈ 0.93, NSE ≈ 0.86, KGE ≈ 0.90), whereas SC2 (SPAD + fertilization) produced the lowest prediction error on the independent test subset, indicating the most robust generalization. Basal fertilization with 60 and 120 kg N ha−1 significantly increased yield in 2019 and 2020, while irrigation generally enhanced yield except for the 30 kg N ha−1 top dressing applied at the V6–V12 stages. These results demonstrate that coupling SPAD measurements with MLP modeling and multi-criteria performance evaluation can support more efficient, site-specific nitrogen and irrigation decisions and help stabilize maize yields under variable climatic conditions.

AgriEngineering doi: 10.3390/agriengineering8050183

Authors: Fábio Henrique Rojo Baio Cid Naudi Silva Campos Larissa Pereira Ribeiro Teodoro Job Teixeira de Oliveira Simone Pereira da Silva Baio Dthenifer Cordeiro Santana Fernanda Ganassin Dilier Olivera Viciedo Paulo Eduardo Teodoro

The application of boron in soybeans in Oxisols of the Brazilian Cerrado is frequently integrated into complex tank fertilizer mixtures with multiple components via foliar application. This study investigated the interactive effects of varying spray application rates (40, 70, 100, and 130 L ha−1) and adjuvant types (organosilicone surfactant; methylated seed oil; and a water control) on boron deposition and the resulting physiological status. The organosilicone surfactant provided superior technical stability and deposition efficiency, allowing for a reduction in application rates to volumes between 40 and 70 L ha−1 maintaining a stable foliar B status across the evaluated range. In contrast, the performance of the methylated oil was strictly dependent on physical deposition, being effective only at intermediate rates, while the use of water alone represented a high risk of technical failure at reduced volumes. Furthermore, the NDRE index proved to be more responsive and robust than NDVI for monitoring delivery efficiency in high-density canopies, as it avoided signal saturation. Finally, Multivariate Analysis helped to observe that soybean yield in the Cerrado is primarily governed by the mitigation of water and thermal stress (TVDI), with optimized boron application acting as a key facilitator of reproductive success and yield stability under these environmental constraints.

AgriEngineering doi: 10.3390/agriengineering8050182

Authors: Pradeep Gupta Sonam Gupta Lipika Goel Abhay Kumar Agarwal Arjun Singh Vijay Shankar Sharma Chiranji Lal Chowdhary Ruchita Chowdhary

Tomato leaf diseases represent a persistent threat to global food security, causing annual crop losses of 20% to 40%. Although deep learning models achieve accuracies exceeding 95% in centralized settings, their deployment across distributed farms is constrained by data privacy concerns, communication bottlenecks, and heterogeneous data quality. This paper proposes Personalized, Clustered, and Communication-Efficient Federated Learning (PCE-FL), a framework that integrates three synergistic components: (1) server-side client clustering to group farms with similar data distributions for personalized model training; (2) federated knowledge distillation to reduce communication overhead by over 91%; and (3) reputation-based aggregation to ensure robustness against unreliable contributions. Extensive experiments on realistic non-IID simulations of the PlantVillage tomato dataset Dirichlet(α∈{1.0,0.5,0.1}) demonstrate that PCE-FL achieves 89.1% accuracy under extreme heterogeneity (α=0.1), surpassing FedAvg by 10.9 and IFCA by 4.8 percentage points, while maintaining a 91% reduction in communication cost. All improvements are statistically significant (p<0.001). These results advance the practical deployment of privacy-preserving collaborative AI in resource-constrained agricultural environments.

AgriEngineering doi: 10.3390/agriengineering8050181

Authors: Kalin Hristov Atanas Z. Atanasov Daniel Lyubenov Chavdar Vezirov

The study addresses the challenge of accurately simulating and visualising the kinematics of agricultural machinery during field operations. The research is motivated by the current lack of comprehensive guidelines for selecting optimal movement and turning modes under varying forward speeds, working widths, and field geometries. A spreadsheet-based environment was utilised to perform simultaneous kinematic simulation and trajectory visualisation. Turning manoeuvres were modelled using smooth composite curves, consisting of straight segments, clothoids, and circular arcs, with trajectories represented in a Cartesian coordinate system through geometric transformations including translation, rotation, and mirror symmetry. Continuity between curve elements was ensured by dimensional chains linking abscissas, ordinates, and direction angles at their start and end points. The influence of key operational factors—forward speed, angular turning velocity, working direction, and field boundaries—was evaluated for a range of turn types, including semicircle, pear-shaped, figure-eight, side exit, U-turn, and P-turn manoeuvres. Field experiments conducted on selected patterns confirmed that the proposed approach can reproduce actual trajectories with sufficient practical accuracy. These results demonstrate that spreadsheet-based kinematic modelling is a robust and accessible tool for optimising tractor–implement movement, enhancing operational planning, and providing a reliable framework for further research into machinery performance under complex field conditions.

AgriEngineering doi: 10.3390/agriengineering8050180

Authors: Guido F. Botta Alejandra Ezquerra Canalejo David Rivero Diego G. Ghelfi Sergio Rodríguez Diogenes L. Antille

Removing subsoil compaction caused by agricultural traffic is energy-demanding and therefore expensive. Experimental work was undertaken on a Typic Argiudoll to quantify the energy required to remove subsoil compaction and determine the associated effects on yield and profitability. The following treatments were compared: (T1) soil under no-tillage for 20 years, which was used as a control; (T2) deep tillage performed with a paratill on soil that had had no-tillage in the 20 years prior to this study; and (T3) deep tillage performed with a chisel plow on soil that had had no-tillage in the 20 years prior to this study. The paratill and chisel plow were operated at depths of 400 and 250 mm, respectively, and the energy required to perform both (deep tillage) operations was determined. Soil cone index and maize yield were measured over three growing seasons and compared with T1. Results showed that the effect of deep tillage lasted for two years, after which the soil reconsolidated reaching soil strength values comparable to their pre-treatment condition. The reconsolidation of tilled soil over this period was due to both natural settlement and post-treatment (random) machinery traffic. The paratill treatment significantly increased maize yield compared with no-tillage, which therefore improved crop gross margins across all three seasons. The chisel plow treatment increased crop yields compared with no-tillage, but yield differences were small and therefore the average crop gross margins were not significantly different. Deep tillage with paratill costed US$76 per ha and generated an average gross income of US$1134 per ha, whereas deep tillage with chisel plow costed US$29 per ha and generated an average gross income of US$1027 per ha. These results compared with an average gross income of US$1001 per ha obtained under no-tillage. If (strategic) deep tillage needs to be performed on long-term no-tillage soil to remediate compaction, paratill may be preferred to chisel plow, but care should be exercised not to re-compact the soil after the operation has been performed. One effective way to do this is by implementing controlled traffic.

AgriEngineering doi: 10.3390/agriengineering8050179

Authors: Bruna Miguel Cardoso João Pedro da Silva Francisco Nelson Câmara de Souza Júnior César Henrique Alves Seleguin Barbara Nairim Ceriani de Luna Maiara Luzia Grigoli Olivio Liliane Santos de Camargos Orivaldo Arf

Nitrogen (N) is a key nutrient for upland rice (Oryza sativa L.), and plant growth-promoting rhizobacteria (PGPR) have been investigated as a sustainable strategy to improve plant nutrition and crop performance. This study evaluated the effects of N topdressing and PGPR inoculation on leaf chlorophyll index (LCI), leaf nutrient concentrations, and yield components in upland rice. A field experiment was conducted in a randomized block design (4 × 6 factorial) with four N rates (0, 40, 80, and 120 kg ha−1) and five PGPR strains (Azospirillum brasilense, Nitrospirillum amazonense, Bacillus subtilis, Priestia aryabhattai, and Methylobacterium symbioticum), plus a non-inoculated control. No significant interaction between N rates and PGPR inoculation was observed. Nitrogen increased leaf phosphorus (P), potassium (K), and magnesium (Mg) concentrations and panicle number; however, it also increased unfilled grains, reduced grain weight, and did not affect grain yield. Azospirillum brasilense increased LCI by 25.7%. Bacillus subtilis and A. brasilense increased leaf N, K, Mg, copper (Cu) and manganese (Mn) concentrations. Azospirillum brasilense, B. subtilis, N. amazonense, and P. aryabhattai reduced unfilled grains, increased grain weight and grain yield by up to 10.7%, whereas M. symbioticum did not differ from the control in grain yield. Under the conditions of this study, nitrogen was not limiting for grain yield, and all strains, except M. symbioticum, were associated with increases in grain yield and changes in plant nutritional status.

AgriEngineering doi: 10.3390/agriengineering8050178

Authors: Dongdong Gu Jiahan Zhang Yuhan Wang Xiaomei Zhang Xiao Xiao Jie Yang Huan Song

To address the poor mechanical adaptability of conventional equipment to 40 cm narrow-row sesame cultivation and the high weeding resistance and energy consumption of traditional weeding tools, this study developed an integrated bionic weeding and plant protection vehicle. The vehicle features a modular structure capable of three-row weeding and four-row plant protection, coupled with an extended-range hybrid powertrain. Its parallel linkage design enables terrain adaptation, ensuring consistent weeding depth of 3–6 cm and stable spraying height. Combined with an adjustable spraying width and a “detection–feedback–adjustment” mechanism to prevent plant collisions, the vehicle is fully compatible with the agronomic requirements of narrow-row cultivation. Inspired by mole cricket forelegs, the vehicle’s bionic weeding wheel blade model incorporates quantified biological features: quadratically fitted claw toe contours (R2 > 0.97), a toe base height-to-width ratio of 1:2, and a toe groove radius-to-toe height ratio of 1:1. This design achieves a reliable biological-to-engineering translation. EDEM-based Discrete Element Method (DEM) simulations confirm that the bionic wheel outperforms conventional designs: the average torque is 17.4% lower (7.75 vs. 9.38 N·m), the soil disturbance rate is 8.2 percentage points higher (95.2% vs. 87.0%), and soil particle motion is more ordered (average velocity: 0.52 vs. 0.58 m/s), effectively reducing energy waste and improving weeding efficiency.

AgriEngineering doi: 10.3390/agriengineering8050177

Authors: Wilson Saltos-Alcivar Cristhian Delgado-Marcillo Ezequiel Zamora-Ledezma Carlos A. Rivas Henry Antonio Pacheco Gil

Modern agriculture must balance productivity with sustainability. In this context, unmanned aerial vehicles (UAVs) offer flexible, cost-effective tools for crop and soil monitoring in precision agriculture. This study aimed to evaluate the potential of UAV-borne RGB imagery, combined with vegetation indices and machine learning, to estimate surface soil properties and crop physiological traits in peanut (Arachis hypogaea) cultivation. A factorial field experiment with four varieties, two planting densities, and two tillage systems was monitored using high-resolution RGB orthomosaics acquired at key phenological stages. From these images, 17 RGB-based indices were computed and related to soil variables and crop traits using Spearman correlation and two regression algorithms: Random Forest (RF) and k-Nearest Neighbors (KNN). RF models outperformed KNN, with the Red Chromatic Coordinate (RCC) index achieving an R2 of 0.87 for predicting soil organic matter content. Indices such as visible NDVI and the Green Vegetation Index also provided robust estimates of canopy condition and leaf chlorophyll. Overall, the results demonstrate that UAV RGB imagery, processed through simple vegetation indices and RF models, constitutes an effective, low-cost approach for monitoring key agronomic parameters in peanut farming.

AgriEngineering doi: 10.3390/agriengineering8050176

Authors: Željko Barač Ivan Plaščak Tomislav Jurić Eleonora Desnica Danijel Jug Monika Marković

This study analyzes whole-body vibration (WBV) exposure of an agricultural tractor operator during three different primary tillage systems: Standard Tillage (ST), Conservation Tillage Deep (CTD), and Conservation Tillage Shallow (CTS). Measurements were conducted in accordance with ISO 2631-1 and ISO 2631-4 along three orthogonal axes (x, y and z) at the operator’s seat. Descriptive and inferential statistical analyses indicate that while none of the mean vibration values exceeded the regulatory limit value of 1.15 m/s2 defined in Directive 2002/44/EC, several measurements—particularly in the y-axis during ST (0.715 m/s2)—surpassed the exposure action value of 0.5 m/s2. These findings suggest that prolonged daily exposure under similar operational conditions may pose long-term health risks for tractor operators. The highest mean WBV values were recorded in the x- and y-axes during CTS (0.354 m/s2 and 0.446 m/s2, respectively), whereas the z-axis exhibited the highest values during ST (0.426 m/s2). Conservation Tillage Deep (CTD) demonstrated the most favorable vibration profile in the vertical axis (0.344 m/s2), indicating its potential dual benefit for soil structure preservation and operator ergonomics. Although all measured values remained below the regulatory limit, the frequent exceedance of the action value underscores the importance of exposure time management, regular maintenance of suspension systems, and implement selection as practical mitigation strategies. This comparative assessment provides baseline WBV data for reduced-tillage systems on hydromorphic soils and offers axis-specific guidance for optimizing operator comfort in sustainable mechanization practices.

AgriEngineering doi: 10.3390/agriengineering8050175

Authors: Ibukunoluwa Ajayi-Banji Kenneth Hellevang Jasper Teboh Szilvia Yuja Ewumbua Monono

Early frost during the R6 and R7 maturity stages of soybean (Glycine max L.) usually causes immature (green or semi-green) crops to be harvested. These immature soybean seeds have a shrunken appearance, green tone, and high chlorophyll content in the oil, leading to heavy discounts for farmers at the elevator. Previous lab-scale storage studies have shown that seed color can change under light and warm temperatures; however, light cannot be added to a commercial storage bin. Therefore, this study examined the effect of field drying and storage conditions on immature soybean color and oil quality. Soybean planted in two plots were desiccated at the R6 and R7 maturity stages and then allowed to field dry. The field-dried desiccated soybeans were conditioned to moisture contents (MCs) of 12 and 17% and stored in airtight plastic bags at respective temperatures of 4 °C and 22.5 °C for 24 weeks. Seed color, mold, and oil quality were analyzed at intervals of 0, 4, 8, 16, and 24 weeks. The desiccated R6 seeds’ color “a” value significantly changed during field drying from (−9.75 to +0.19) and (−8.96 to +1.95) for Plot 1 and Plot 2, respectively. This means that the color changed from green to a golden yellow or light greenish-brown color after field drying. The chlorophyll content of the desiccated soybeans after field drying at the two maturity stages for both plots was less than 3 mg kg−1 of oil and was relatively stable throughout storage. During storage, at 17% moisture content and 22.5 °C, mold counts increased significantly for R6, R7, and R8 (frozen) control soybeans between weeks 0 and 4 to 4.36 CFU g−1, 5.93 CFU g−1 and 6.22 CFU g−1, respectively. Peroxide and free fatty acid values were within acceptable limits across all storage temperatures and moisture contents. This study suggests that favorable weather conditions for field drying after an early frost have the potential to improve the color of harvested and stored soybeans, similar to mature soybeans.

AgriEngineering doi: 10.3390/agriengineering8050174

Authors: Luca Bernini Michele Monno

The agriculture and food processing sectors are essential, meeting the fundamental needs of global populations. However, it is crucial to adopt sustainable practices that fulfill these needs while minimizing environmental impact. Climate change, once a theoretical concern, is now an urgent and tangible challenge, requiring immediate action to mitigate its effects. As such, all human activities, particularly those in resource-intensive sectors like agriculture, must be reevaluated. This study explores and reviews the potential of applying waterjet systems and their evolution in agricultural and food processes to improve efficiency and minimize resource consumption; while the use of pure waterjet technology for soft foods has emerged as an established practice, its extension to agricultural applications and the use of abrasive waterjet in this field are still in the research and experimentation phase. This work presents preliminary results, discussing the key waterjet components, their economical modeling, and food safety. Three main categories of applications—cutting of soft, plant-based products, cutting of animal products, and in-field agricultural applications—are reviewed, with detailed use cases on strawberry de-calyxing, meat–bone cutting and sugarcane harvesting, respectively. These applications are analyzed by highlighting waterjet main advantages in terms of cutting performance, as well as food quality and preservation. At the end, future directions are delineated, suggesting potential advancements that could allow us to replace traditional methods with more innovative and sustainable alternatives. A specific focus is given to abrasive ice waterjets.

AgriEngineering doi: 10.3390/agriengineering8050173

Authors: Rosalra Maria Alves de Morais Gabriel Araújo e Silva Ferraz Rafael de Oliveira Faria Mirian de Lourdes Oliveira Arthur Correia de Noronha

Studies related to the mechanization of coffee transplanting combined with precision agriculture techniques demonstrate a need to verify the quality of the operation to optimize future production processes in the field and reduce costs. The objective of this study was to analyze the operational costs arising from a semi-mechanized coffee transplanting process using an autopilot. The study was conducted on a rural property in the municipality of Santo Antônio do Amparo—MG—where 7458 coffee seedlings were transplanted using a planting platform pulled by a tractor equipped with an autopilot and a GNSS antenna, over a period of 4 days. The date and time data of the operation, recorded every second by the autopilot, were collected and recorded in spreadsheets to assist in calculating operational costs. Two semi-mechanized transplanting scenarios were compared: one using autopilot and the other using conventional semi-mechanized transplanting. The results indicated that the hourly cost of operation with autopilot was US$2130.42 h−1, while the conventional system presented US$326.03 h−1. The effective operational cost was US$3975.61 ha−1 for the system with autopilot and US$442.31 ha−1 for the conventional system in 2020. After monetary updating to 2025, the operational costs increased to US$1845.19 ha−1 and US$116.28 ha−1, respectively. The investment analysis indicated an Internal Rate of Return of 89.7%, highlighting the potential return on investment in the study. Therefore, it was emphasized that even with a high investment cost, the application of autopilot is viable for improving tractor steering during operation and ensuring uniformity in the positioning of coffee seedlings.

AgriEngineering doi: 10.3390/agriengineering8050172

Authors: María P. Iglesias Muditha K. Heenkenda Kerin F. Romero

Weed interference during early growth stages is a major constraint on sugarcane productivity, yet effective tools for species-specific detection remain limited in tropical agricultural systems. This study evaluated the spectral separability between Sugarcane (Saccharum officinarum) and a dominant weed species, Rottboellia cochinchinensis, to develop an accessible framework for early-stage weed mapping. Multispectral data acquired from an Unmanned Aerial Vehicle (UAV) and hyperspectral data obtained from a field spectrometer were utilized. Hyperspectral data were synthesized to reconstruct multispectral bands (UAV image bands) using a regularized linear synthesis model, thereby generating spectral signatures. Spectral separability between sugarcane and Rottboellia cochinchinensis was assessed visually and statistically (Jeffries–Matusita distance). Blue and Green bands provided the strongest differentiation between species, while RedEdge enhanced separability when paired with pigment-sensitive wavelengths. When using vegetation indices based on the near-infrared (NIR) band, the visual appearance of class separation was poor due to the NIR band’s sensitivity to variation in leaf internal structure, canopy architecture, water content, and spectral mixing with the soil background at the early stage of sugarcane. These results were used to differentiate weed coverage from sugarcane. Object-based image analysis (OBIA) outperformed the pixel-based method, achieving higher overall accuracy (0.9038) and a more spatially coherent weed delineation (Kappa = 0.8499). These findings suggest that synthesized spectral signatures of Rottboellia cochinchinensis and sugarcane, combined with targeted spectral indices and OBIA techniques, offer a practical and transferable approach for early detection of Rottboellia cochinchinensis at the farm level.

AgriEngineering doi: 10.3390/agriengineering8050171

Authors: Jakhfer Alikhanov Aidar Moldazhanov Akmaral Kulmakhambetova Dmitriy Zinchenko Tsvetelina Georgieva Eleonora Nedelcheva Plamen Daskalov

This article presents methods and hardware for the multi-criteria non-destructive determination of chicken egg quality parameters, implemented using a multifunctional rotary system. Unlike traditional single-criteria sorting, which relies primarily on weight, the proposed approach utilizes a combination of physical and geometric parameters, including weight, linear dimensions, cross-sectional area and perimeter, volume, density, and shape. The experimental framework for the study was formed by measuring the parameters of 750 chicken eggs, covering the entire range of product categories and morphological variations. Geometric parameters were determined using machine vision methods, weight was determined using a strain gauge, and derived parameters were calculated using formalized models. A multi-criteria evaluation algorithm based on fuzzy set theory was used to make the classification decision, accounting for overlapping feature ranges and regulatory differences between EU and EAEU standards. The results of statistical and correlation analysis showed that egg density is identified as a relatively independent diagnostic parameter, weakly correlated with weight and geometric characteristics, justifying its inclusion in the quality model. A comparison of manual and automatic classification revealed differences in boundary categories during single-criteria sorting and indicated the potential of a multi-criteria approach. The obtained results support the feasibility of the developed methods and hardware under the conditions of the present study.

AgriEngineering doi: 10.3390/agriengineering8050170

Authors: Nitin Bhatia Maxence Plouviez

Hyperspectral airborne data combined with machine learning has proven effective for characterizing plant nutritional quality. However, terrain, viewing geometry, and illumination can distort spectral signatures, leading to biased models with limited generalizability for large-scale mapping across farms with a heterogeneous landscape. In this study, we developed a framework for mapping pasture quality using airborne hyperspectral imaging while explicitly accounting for in-field acquisition and environmental effects. Nitrogen content (N%) and metabolizable energy (ME) were used as reference indicators across four hill country farms in New Zealand with contrasting environmental and management conditions. Ground truth was obtained using standard laboratory wet chemistry methods and paired with AisaFENIX airborne hyperspectral data, resulting in 1610 spectral samples derived from 161 spatially independent ground plots. Gaussian Process Regression (GPR) and a one-dimensional convolutional neural network (1D-CNN) were trained and evaluated on an independent test dataset. Both models achieved strong predictive performance (R2 > 0.8); however, GPR provided more reliable estimates through predictive uncertainty. Using a 95% confidence interval threshold to mask uncertain predictions increased overall performance (R2 > 0.9) and consequently improved the reliability of the mapped outputs. This approach enables spatially explicit pasture nutrient assessment to support precision land management for carbon and nitrogen.

AgriEngineering doi: 10.3390/agriengineering8050168

Authors: Darkhan Karmanov Askhat Umbetbekov Zauresh Tulyubaeva Jenis Utemuratov Akbota Duisengali Nurgul Seiitkazy

Pre-sowing tillage under irrigated agriculture is associated with high energy demand and increased risk of soil structural degradation, particularly in heterogeneous loam soils of arid and semi-arid regions. This study presents the engineering optimization and field validation of a combined implement for single-pass rotary strip tillage and precision seeding developed for irrigated sierozem soils of Southern Kazakhstan. The research integrates analytical modeling of soil–blade interaction, optimization of rotary blade geometry, and comparative field experiments using an experimental prototype (FS-2.1). Analytical optimization identified an optimal blade installation angle of 54–56°, resulting in an approximately 22% reduction in specific cutting area. Field results demonstrated that the single-pass system formed a high-quality seedbed, with 85.2% of soil aggregates smaller than 25 mm and a surface leveling deviation below 5 mm. Compared with a conventional multi-pass technology, traction load, fuel consumption, and total energy input were reduced by 38%, 43%, and 54.5%, respectively. The results confirm that combining optimized rotary blade geometry with strip-based soil disturbance enables substantial energy savings without compromising agronomic performance. The proposed engineering solution provides a reproducible framework for low-traction, resource-efficient tillage–seeding systems suitable for irrigated agriculture in Southern Kazakhstan and comparable agroecological regions.

AgriEngineering doi: 10.3390/agriengineering8050167

Authors: Éva Hajnal

Rumen bolus sensor technology has been applied for roughly a decade in the precision livestock farming of ruminants. Despite its substantial potential, widespread adoption remains limited, largely due to the incomplete functionality of current sensors, their relatively high cost, and the still insufficient scientific understanding of the physiological and husbandry-related parameters they are intended to measure. This article reviews the capabilities of rumen bolus sensors, their technical and informatic foundations, their role within precision livestock farming, and the results reported to date. It further examines how the functions and applications of bolus sensors align with the Sustainable Development Goals (SDGs 2, 3, 8, 9, 12, and 13), as well as the sustainability challenges associated with their use. Finally, the paper identifies potential technological development pathways that could enable rumen bolus sensors to become highly effective and widely adopted tools in the future.

AgriEngineering doi: 10.3390/agriengineering8050169

Authors: Thanushan Kirupairaja A. Salim Bawazir

Effective management of reservoir water for irrigation is crucial in arid regions prone to drought and water shortages. However, evaporation losses from reservoirs remain poorly understood. Direct measurements typically quantify evaporation only at the measurement site rather than across the entire reservoir. This study introduces the Turbulent Exchange Approach for Reservoir Evaporation Estimation (TEAREE). The TEAREE is a simple model that integrates a bulk aerodynamic formulation with Landsat 8–9 satellite water-surface temperature data and meteorological observations to estimate spatially distributed daily reservoir evaporation. The TEAREE model was first evaluated at Elephant Butte and Caballo reservoirs in NM, USA, and subsequently applied across multiple reservoirs with diverse climatic conditions to demonstrate its applicability for estimating open-water evaporation. Daily evaporation was obtained by upscaling satellite overpass-time evaporation estimates using the daily-to-instantaneous vapor pressure deficit ratio (ke) and wind speed. The model performed strongly across 12 lakes (R2 = 0.91–0.99; RMSE = 0.27–0.85 mm/day) compared with the bulk aerodynamic (B_AER) method. Comparison with eddy covariance (EC) evaporation also showed good agreement. Monte Carlo analysis indicated moderate uncertainty associated with ke variability, supporting the operational use of a constant ke = 0.95 for daily upscaling.

AgriEngineering doi: 10.3390/agriengineering8050166

Authors: Haojie Zhang Bing Qi Yunxia Wang Shutong Huang Youqiang Ding Wenyi Zhang

Aiming at the problems of mutual overlapping in high-throughput seed flow and difficulty in accurate detection of seeding rate during high-speed precision wheat seeding, a double-layer orthogonal through-beam photoelectric detection device for high-throughput wheat seed flow was developed in this paper, based on a four-layer staggered hook-type precision wheat seed-metering device. Combined with the least squares method for threshold optimization and an error compensation model, the detection accuracy was effectively improved. Bench test results show that the detection accuracy of the device is stable above 97% at medium and low seeding frequencies of 20–40 Hz, which can meet the requirements of conventional operations. When the seeding frequency increases to 80–120 Hz, the accuracy decreases to 89.05% due to the increase in seed flow density. After introducing the compensation model, the accuracy remains above 95% in the high-frequency range of 90.2–140.2 Hz, which is nearly 10 percentage points higher than that without compensation. The research results can provide effective support and a technical approach for the accurate online detection of high-frequency seed flow in high-speed precision wheat seeding.

AgriEngineering doi: 10.3390/agriengineering8050165

Authors: Franck Morais de Oliveira Verónica González Cadavid Jairo Alexander Osorio Saraz Felipe Andrés Obando Vega Gabriel Araújo e Silva Ferraz Patrícia Ferreira Ponciano Ferraz

Pig weighing is an essential procedure for monitoring growth and animal health; however, conventional methods are often labor-intensive, costly, and potentially stressful. In this context, this study proposes a non-invasive approach for estimating the body weight of pigs during the growing stage based on computer vision and the YOLOv11 algorithm, enabling automatic segmentation and individual identification in multi-animal environments. The study used RGB images of 10 group-housed pigs captured throughout the growing phase, in which automatic dorsal segmentation was combined with individual identification through numerical markings. From the generated binary masks, the segmented dorsal area was extracted and used as a predictor variable in Linear Regression and a Multilayer Perceptron (MLP) Artificial Neural Network. The YOLOv11 model showed consistent performance in the segmentation task, achieving test-set metrics of Precision = 0.849, Recall = 0.886, mAP@0.50 = 0.936, and mAP@0.50–0.95 = 0.819, demonstrating good generalization capability in scenarios with intense animal interaction. In the weight prediction stage, Linear Regression and the MLP achieved high coefficients of determination (R2 = 0.96 and 0.95, respectively) with low errors (RMSE = 1.52 kg and 1.63 kg; MAE = 1.20 kg and 1.25 kg), indicating a strong correlation between segmented dorsal area and actual body weight. Class-wise analysis revealed superior performance for classes 7 and 9, with R2 values up to 0.98 and RMSE below 1.1 kg, whereas class 8 showed greater error dispersion, associated with higher morphological variability and a smaller number of available samples. These results demonstrate that the direct use of morphometric information extracted from segmented masks in 2D images constitutes a robust, accurate, and low-cost approach for automatic pig body-weight estimation. Moreover, this study is among the few addressing this task specifically during the growing stage, highlighting its potential for future deployment in embedded systems and intelligent monitoring platforms for precision pig farming, although further evaluation of computational efficiency and real-time performance is still required.

AgriEngineering doi: 10.3390/agriengineering8050164

Authors: Weirong Huang Jianhua Xie Silin Cao Jiahong Tang Yi Yang

Regarding the current issues in Xinjiang, China, during the harvesting of cotton stalks, the lack of specialized, efficient, and durable cutting blades for cotton stalks causes uneven cutting, high power consumption, and short blade life. In this study, a biomimetic serrated blade was designed based on the Trictenotomidae mandible for efficient, low-power-consumption cutting. The biomimetic design, FEM-SPH coupled simulation, bench test, combined with response surface methodology, and field test were used. The simulation results showed that under the same working conditions, the maximum shear stress was 34.81% lower than that for the ordinary blade and 22.05% lower than that for the ordinary serrated blade. And the bench test results showed that cutting power consumption was reduced by about 20.12% and 15.69% compared to the ordinary cutting blade and serrated cutting blade, respectively. When cutting velocity was 1.3 m/s, cutting inclination angle was 11°, and ratio of cutting velocity and feeding velocity was 1.1, the biomimetic serrated cutting blade could achieve effective cutting of cotton stalks and obtain better quality of cutting—the cutting power per unit area and the cutting-edge angle after cutting cotton stalks were 52.08 kJ/m2 and 6°, respectively. The research results can provide a theoretical basis and support for the utilization of cotton stalks out of the field, as well as the cutting of other similar crop stalks.

AgriEngineering doi: 10.3390/agriengineering8050163

Authors: Muhammad Afzaal Fawad Azeem Zulfiqar Memon

Crop cultivation involves a series of procedures from sowing till harvesting, making it a time-consuming activity. The crop cycle typically spans four to six months, during which cultivation outcomes are influenced by dynamic environmental and management factors such as water availability, temperature, and humidity. These parameters are collectively referred to as Optimum Cultivation Factors (OCFs). Once the cultivation process starts, poor OCFs may lead to reduced crop growth, leading to heavy economic loss. Historically, lessons learned from previous cultivation cycles have been a primary source for improving agricultural practices. Developing simulators that mimic agricultural environments in a controlled setting can support the analysis of cultivation factors while reducing time and resource requirements. In this study, a multifunctional agricultural simulator with a network of actuators is developed in the MATLAB/Simulink environment. The designed simulator mimics the agricultural field’s real-time environment while maintaining the temperature, humidity, and moisture content with appropriate water provision. Based on real field environmental data, the fuzzy-based membership functions are designed to emulate outdoor agricultural conditions at the laboratory scale. The designed system monitors and controls the actuators, such as water pumps for moisture, a heater for temperature, and a sun simulator for solar irradiation control. The cascaded fuzzy logic controller enables multi-factor environmental assessment by analyzing actuator responses under varying operating conditions, supporting pre-cultivation decision making.

AgriEngineering doi: 10.3390/agriengineering8050162

Authors: Rosa Janette Pérez-Chimal Claudia Angélica Rivera-Romero Julián Moisés Estudillo-Ayala Remberto Sandoval-Aréchiga Alejandro Barrientos-García Jorge Ulises Muñoz-Minjares

Objectives: Multispectral imaging and spectrophotometry are widely used to estimate plant physiological characteristics, yet the literature remains fragmented across sensors, indices, and analytical approaches. Methods: This systematic review followed PRISMA 2020 and was preregistered in OSF (Open Science Framework). Web of Science, Scopus, Google Scholar, and Consensus were searched up to January 2025 for peer-reviewed studies and selected gray literature studies focused on plant physiological trait estimation using multispectral or spectrophotometric methods. From 256 identified records, 96 studies met the eligibility criteria. Methodological quality was assessed across five domains, and results were synthesized narratively owing to high heterogeneity. Results: A total of 96 studies met the eligibility criteria. Among these, multispectral sensors were the most commonly used (40.7%), followed by UAV-mounted platforms (25.9%), while hyperspectral sensors accounted for 18.5% of the studies. The most frequently used vegetation index was NDVI, reported in 87% of the studies, mainly for estimating vigor, biomass, and canopy structure. Discussion: Although multispectral indices reliably capture key agronomic traits, cross-study comparability is currently hampered by significant methodological variability and a lack of consistent validation protocols. Conclusions: Multispectral imaging and spectrophotometry are effective tools for estimating plant physiological traits, but greater standardization is needed across studies. Owing to the limited number of studies on Stenocereus spp., the review was expanded to plants in general; the shortage of reports addressing Stenocereus spp. highlights the need for future research in these species.

AgriEngineering doi: 10.3390/agriengineering8050161

Authors: Gift Siphiwe Nxumalo Tondani Sanah Ramabulana Noxolo Felicia Vilakazi Attila Nagy

South African irrigation schemes face critical challenges of water scarcity, infrastructure deterioration, and limited monitoring capacity, threatening agricultural productivity and food security. This scoping review systematically analyses 59 peer-reviewed publications (2000–2025) on sensor-based and acoustic irrigation monitoring technologies in South Africa, using transformer-based natural language processing (Sentence-BERT embeddings), unsupervised Machine Learning (UMAP dimensionality reduction, HDBSCAN clustering), and geospatial mapping applied to literature retrieved from Web of Science and Scopus. Results show that water quality monitoring (42.4% of studies) and remote sensing (25.4%) dominate the national research landscape, while soil moisture sensing and modelling remain comparatively limited. Notably, no peer-reviewed studies applying acoustic monitoring technologies to irrigation were identified, representing a critical gap despite proven international applications for leak detection (95–98% accuracy), widespread infrastructure aging (over 50% of schemes exceeding 30 years), and reported water losses of 30–60% in poorly managed systems. Reported experimental water savings range from 15% to 30%, yet applications remain largely confined to pilot-scale implementations concentrated within a limited number of Water Management Areas. Persistent adoption barriers include infrastructure unreliability, financial inaccessibility, limited digital literacy, and weak institutional coordination. The review recommends: (i) expanding research coverage across underrepresented regions and Water Management Areas; (ii) strengthening extension support and technical training to enable broader adoption; and (iii) integrating low-cost sensor networks with predictive, data-driven irrigation advisory systems. These priorities aim to support scalable, context-sensitive irrigation modernisation under increasing water scarcity pressures.

AgriEngineering doi: 10.3390/agriengineering8050160

Authors: Yuheng Li Xiafen Zhang

Early plant leaf disease detection and timely control is important for agricultural yield and stability. Yet, it is difficult for manual labor to monitor the health of the plant leaf 24 h a day. Existing detection approach cannot meet the demands of texture enhancement features. Therefore, this paper proposes a new detection approach which undergoes three-layer transformations: convolutional layer, attention mechanism layer and loss function layer. Firstly, ADown is used to extract fine-grained texture features from suspected leaves to reduce computational load. Secondly, Gabor texture enhancement is proposed to extract and enhance the contour and the directional texture of suspected areas using multi-directional filtering, followed by a combination Transformer to enhance the global context modeling capability. Thirdly, a dynamic boundary loss function (DBL) is employed to dynamically adjust the probability distribution of bounding box regression through adaptive temperature coefficient and information entropy, thereby improving the positioning accuracy of the detection box. The experiments show that ATD-Net achieved an average accuracy of 87.42% (mAP50) and an accuracy of 85.96%, with a computational complexity of 6.5 GFLOPs. The visualization results and ablation experiments show that the collaborative work of the proposed modules significantly improves the detection robustness in complex backgrounds, early diseases, and small target scenes. Compared to the original model, ATD-Net achieves a performance improvement of 1.1% at mAP50 and a speed increase of 17.7%. The model size remains almost unchanged, at 5.2 MB. It is an efficient and promising solution for future real-time disease recognition in complex agricultural environments.

AgriEngineering doi: 10.3390/agriengineering8040159

Authors: Erki Kivimeister Risto Ilves Kersti Vennik Jüri Olt

Soil penetration resistance (Pr) measurement is important for assessing compaction and permeability; however, Pr is heavily dependent on soil moisture. Therefore, the interpretation of Pr data is significantly more reliable if moisture is measured simultaneously and in the same soil layer. In addition, reliable assessment of permeability requires consideration of both soil moisture and penetration resistance. The aim of this work was to develop a prototype of a hand-held combined device in which a volumetric moisture sensor operating on the principle of a galvanic cell is integrated into the Pr measurement cycle, allowing simultaneous measurements at different depths. The device simultaneously determined the penetration resistance acting on the cone, the measurement depth (with a laser sensor), the volumetric moisture (Cu–Zn electrode pair), and the location of the measurement site (GNSS). The moisture sensor was found to be neutral to the influence of the mineral part of the soil on moisture measurement, which in the case of other alternative measurement methods significantly affects the soil moisture measurement data. The calibration of the galvanic moisture sensor was performed under laboratory conditions (VWC 5–50%) based on a gravimetric reference. The relationship was approximately linear at lower moistures and nonlinear at higher moistures. The salinity effect test indicated that the TDR-based reference device gave a strongly overestimated moisture reading in saline soil, while the galvanic cell-based measurement remained within a realistic range compared to the gravimetric method. The results indicate that Pr measurement integrated with a galvanic sensor creates a practical prerequisite for the simultaneous collection of Pr and moisture profiles and is useful in conditions where dielectric methods are affected by salinity or minerality interference.

AgriEngineering doi: 10.3390/agriengineering8040158

Authors: Teofil-Alin Oncescu Sorin Stefan Biris Iuliana Gageanu Nicolae-Valentin Vladut Ioan Catalin Persu Stefan-Lucian Bostina Daniela Tarnita Ana-Maria Tabarasu Daniela-Cristina Radu Cornelia Muraru-Ionel Raluca Sfiru Ionut Cosmin Nica Teodor Anita

The aim of this study is to evaluate the biomechanical response of a seated operator subjected to whole-body vibrations generated by two agricultural tractors with different propulsion systems: a diesel model (TD80D) and an electric prototype (TE-0). An integrated experimental–numerical approach was employed, combining triaxial accelerometer measurements under real operating conditions (constant speed of 5 km/h on unprepared terrain) with random vibration response simulations performed in Altair SimSolid. The excitation input for the numerical model was defined using frequency-dependent power spectral density (PSD) functions derived from experimentally measured acceleration signals and scaled to a representative global RMS value. The analysis focused on the distribution of mechanical stress in key anatomical regions of a virtual human dummy in a seated posture, including the foot sole, knee, lumbar region, and head. The results indicate that, under the analysed conditions, the electric tractor (TE-0) exhibits improved vibration attenuation, leading to significant reductions in mechanical stress across all analysed regions, with decreases of up to 56.3% at the foot sole, 50.0% at the knee, 53.3% in the lumbar region, and 91.1% at the head compared to the diesel tractor (TD80D). These findings highlight the relevance of integrating experimental measurements with numerical simulation for assessing operator exposure to vibrations and suggest that electric tractor configurations may provide improved biomechanical comfort under the analysed operating conditions.

AgriEngineering doi: 10.3390/agriengineering8040157

Authors: Pankaj B. Pathare Peeyush Soni

Agriculture is currently undergoing a significant technological transformation as global food systems respond to escalating challenges associated with population growth, climate variability, resource limitations, and environmental sustainability [...]

AgriEngineering doi: 10.3390/agriengineering8040156

Authors: Baidong Zhao Chenghan Yang Gang Zheng Baurzhan Belgibaev Madina Mansurova Sholpan Jomartova Dingkun Zheng

Precise curvilinear path tracking remains a persistent challenge for autonomous agricultural machines, where conventional Model Predictive Control (MPC) suffers from poor adaptability to varying curvatures and high computational overhead in unstructured farmland environments. This paper proposes a soft-constrained MPC framework enhanced by a two-layer fuzzy architecture and Recursive Least Squares filtering to address these limitations simultaneously. The first fuzzy layer dynamically adjusts the MPC prediction horizon in response to real-time path curvature, enabling proactive steering on complex curved trajectories. The second fuzzy layer tunes the state weighting matrix online based on lateral and heading deviations, improving transient tracking accuracy without increasing computational cost. Recursive Least Squares filtering is further integrated to suppress sensor noise and compensate for tire slip dynamics inherent to farmland operation. The proposed framework is validated using MATLAB simulations on both constant-curvature semicircular paths and variable-curvature S-curve trajectories at operational speeds of 2.0 and 2.5 m/s, followed by outdoor field trials on a scaled autonomous robot platform. Simulation results demonstrate average tracking error reductions of 52.7–55.9% on constant-curvature paths and 10.8–18.2% on variable-curvature paths compared to fixed-parameter soft-constrained MPC. Field experiments confirm practical viability, achieving an RMS lateral error of 0.131 m over a 50 m curved route on natural terrain. These results demonstrate that the hierarchical decomposition of adaptation objectives yields substantial accuracy gains while preserving real-time feasibility on resource-constrained embedded platforms.

AgriEngineering doi: 10.3390/agriengineering8040155

Authors: Francisco Guevara-Fernández Sebastian Casas-Niño Milagros Ninoska Munoz-Salas Wagner Meza-Maicelo Manuel Oliva-Cruz Flavio Lozano-Isla

Microbial bio-inoculants have been proposed as management tools to enhance crop performance under variable environmental conditions; however, their effectiveness is often influenced by site-specific factors. This study evaluated the effects of bio-inoculation on seed germination and seedling vigor of pea under osmotic stress induced by polyethylene glycol (PEG 6000), and its interaction with two fertilization levels (75% and 100% of the recommended dose) under field conditions in the Amazonas region of Peru. Under laboratory conditions, germination percentage remained high across all treatments (93.3–100%) and was not affected by bio-inoculation or osmotic potential; however, osmotic stress altered germination dynamics, increasing mean germination time from 1.85–2.09 days at 0 MPa to 2.26–2.43 days at −0.8 MPa, while germination synchrony and seedling vigor decreased as stress increased. The seedling vigor index reached maximum values at −0.2 MPa (4.47–5.29) and declined at −0.8 MPa (1.50–2.00), and multivariate analyses showed that variation in germination responses was mainly associated with germination timing and vigor rather than seed viability. Under field conditions, no significant effects of fertilization level, microbial bio-inoculation, or their interaction were detected on agronomic traits or yield, although variability between locations was observed; plant height ranged from 38.5–46.3 cm in Lamud and from 100.6–108.3 cm in Molinopampa, while grain yield varied from 698–1846 kg/ha and 8771–9919 kg/ha, respectively. Overall, environmental conditions exerted a stronger influence than microbial bio-inoculation on germination dynamics and field productivity, while the findings provide practical guidance for improving pea production with bio-inoculants and optimized fertilization.

AgriEngineering doi: 10.3390/agriengineering8040153

Authors: Ethan Elliott Allison Foster Ayrton Bernussi Hamed Sari-Sarraf Mohammad Saed Vikki B. Martin Neha Kothari

Cotton, a globally significant crop grown in over 100 countries, sustains a $40 billion market and provides employment for over 350 million people worldwide. However, plastic contamination remains a persistent challenge within the industry, degrading cotton fiber quality and disrupting ginning. Manual inspection and optical machine-vision systems struggle when plastic fragments are concealed by fibers or lack sufficient color contrast. To address these challenges, we developed an ultrasonic phased-array imaging system operating at 40 kHz under field-programmable gate array (FPGA) control. Transmitter elements emit pulsed ultrasound along radial paths, separate reflection receivers record echo amplitudes to form acoustic images, and a set of transmission receivers captures signal attenuation, which is overlaid onto the reflection-based image to highlight potential contaminants. In preliminary laboratory-based tests on both seed cotton and lint samples, the system successfully detected visually obscured plastic fragments as small as 2cm×2cm with an angular resolution limit of ±3°. Distinct reflection peaks and corresponding attenuation overlays were produced across the field of view, validating the system’s detection capabilities. These results demonstrate the feasibility of using ultrasonic imaging to reveal concealed plastics in cotton processing. Integrating this approach with existing optical methods could enhance contaminant-removal workflows and improve overall fiber quality and processing efficiency.

AgriEngineering doi: 10.3390/agriengineering8040154

Authors: Nelson Ken Narusawa Nakakoji Ítala Duam Souza Narusawa Fábio Júnior de Oliveira Welliton de Lima Sena Félix Lélis da Silva Gabriel Garreto dos Santos João Paulo Ferreira Neris Pedro Guerreiro Martorano Alexandre da Trindade Lélis Jose Gilberto Sousa Medeiros Norberto Cornejo Noronha Luís Sérgio Cunha Nascimento Everton Cardoso Wanzeler Jean Marcos Corrêa Tocantins Thais Lopes Vieira João Fernandes da Silva Júnior Paulo Roberto Silva Farias

Black pepper (Piper nigrum L.) is a crop of significant economic importance in the Amazon, especially in the state of Pará, where intensive production systems predominate. Understanding the spatial variability of soil attributes and their relationship with plant vigor is essential to optimize agricultural practices and input use. Geotechnology-based approaches enable the generation of more precise management zones, contributing to efficient resource use and increased profitability. This study aimed to delimit potential management zones in black pepper crops based on the spatial analysis of soil bulk density (BD) integrated with the NDVI (Normalized Difference Vegetation Index), evaluated using the Bivariate Moran’s Index. The research was conducted in a production area in the municipality of Baião, Pará, Brazil, using soil samples to determine bulk density and UAV images for NDVI calculation. Data were interpolated by kriging and analyzed to identify spatial associations between soil compaction and NDVI. Soil bulk density ranged from 1.14 to 1.80 Mg m−3, while NDVI values ranged from 0.07 to 0.91, revealing a clear inverse spatial relationship between soil compaction and vegetative vigor. The integration of BD and NDVI allowed the delineation of site-specific management zones, supporting more efficient decision-making in precision agriculture.

AgriEngineering doi: 10.3390/agriengineering8040152

Authors: Muhammad Irfan Sharif Yong Zhong Muhammad Zaheer Sajid Francesco Marinello

Accurate diagnosis of wheat leaf diseases in real farming conditions requires models that are not only highly accurate but also computationally efficient and interpretable for practical deployment on edge devices. We propose GateProtoNet (GPN), a two-stage, compute-aware, and explainable framework for multi-class leaf disease recognition. Stage-1 performs ultra-light healthy-versus-diseased screening, enabling early exit for healthy samples and substantially reducing average expected inference cost. For diseased samples, Stage-2 applies a novel hybrid backbone featuring a frequency-factorized Discrete Wavelet Transform (DWT) stem, parallel micro-lesion convolutional encoding for fine texture patterns, and a linear token mixer for global context modeling. A cross-gated fusion module adaptively integrates local and global evidence with minimal computational overhead. To ensure trustworthy predictions, GPN introduces a prototype evidence head that performs classification via similarity to learned class prototypes, providing human-interpretable explanations, along with a faithfulness constraint that enforces explanation reliability by measuring confidence degradation under salient region removal. Rigorous evaluation on four publicly available wheat and cotton leaf disease datasets demonstrate that GateProtoNet achieves 99.2% classification accuracy, 99.1% macro-F1 score, and 99.3% AUC, significantly outperforming existing CNN, transformer, and hybrid baselines while requiring substantially fewer parameters and FLOPs. The two-stage inference strategy reduces average computational cost by avoiding full model execution on healthy leaves, enabling real-time, on-device diagnosis for resource-constrained agricultural environments.

AgriEngineering doi: 10.3390/agriengineering8040151

Authors: Kittiphum Pawikhum Yanqiu Yang Long He John A. Pecchia Paul Heinemann

Manual harvesting of white button mushrooms involves coordinated bending and twisting motions to detach the fruiting body while minimizing surface damage; however, replicating these actions in automated systems remains challenging. In this study, a vacuum-based end-effector that mimics manual twist–bend detachment using a single-point contact was designed and evaluated to reduce mechanical damage. Key detachment parameters, including the friction coefficient (mean 0.62), bending angle (average 5.72°), and twisting torque (average 2.56 N·m), were experimentally analyzed to determine the minimum vacuum pressures required for effective bending and twisting, which were −8.64 ± 2.21 kPa and −8.91 ± 2.45 kPa, respectively, with no significant difference observed between the two motions (p = 0.51). A customized vision-based image processing algorithm was developed to quantify postharvest surface damage using a whiteness index (WI). An optimal vacuum pressure of −17.17 kPa was identified, together with a bending angle of 10° and a twisting angle of 90°, balancing high harvesting success with preservation of mushroom quality. The results highlight the influence of end-effector design parameters, including vacuum cup material, contact area, bending direction, and vacuum application duration, on harvesting performance and product marketability, supporting the development of robotic systems for fresh mushroom harvesting.

AgriEngineering doi: 10.3390/agriengineering8040150

Authors: Rabiu Omeiza Isah Segun Emmanuel Adebayo Bello Kontagora Nuhu Eustace Manayi Dogo Buhari Ugbede Umar Danlami Maliki Ibrahim Mohammed Abdullahi Olayemi Mikail Olaniyi James Agajo

Post-harvest losses of fruits and vegetables are an important bottleneck in food systems of countries around the world, with 30–50% of perishable food items lost between farm and consumer, smallholder farmers in low-and-middle income countries (LMICs) with poor cold chain infrastructures facing a disproportionate burden. Evaporative cooling (EC) is a low-cost and energy-efficient alternative to mechanical refrigeration; however, traditional systems are operated in one position and are dependent on climate, which restricts its performance. The combination of Internet of Things (IoT) sensing, machine learning (ML), and the advanced control theory has made intelligent evaporative cooling systems (IECS) adaptive, data-driven platforms that can regulate the environment in real-time and optimise autonomously. This is a systematic literature review that was carried out according to PRISMA 2020, summarising 94 peer-reviewed articles published in 2018–2025 to map the technological landscape, performance indicators, and research directions of the field of post-harvest fruit and vegetable preservation using IECS. Findings indicate that IECS can considerably lower the storage temperatures, increase the shelf life by 50–200%, and reduce energy consumption by 75–90% compared to traditional refrigeration, and the payback period is as short as 1.2 years. In arid conditions, ML models are accurate in prediction with an R2 of 0.98. The gaps in the research identified are a lack of validation in wet climatic conditions, non-existent standardised Ag-IoT protocols, inadequate Food–Energy–Water (FEW) nexus calculation, and no explainable AI (XAI) interfaces. An example of a conceptual framework of four layers synthesised is proposed to direct next-generation research and implementation of the IECS.

AgriEngineering doi: 10.3390/agriengineering8040149

Authors: Marcos Elias de Oliveira Alexandre Ferreira do Nascimento Ericka Aguiar Carneiro Guillaume Francis Bertrand Lúcio André de Castro Jorge Érick Rúbens Oliveira Cobalchini Edson Wendland Valéria Peixoto Borges Davi de Carvalho Diniz Melo

Understanding crop water use is essential for improving agricultural water management and ensuring sustainable food production, especially in regions with limited water resources. Evapotranspiration (ET) is a key component of the hydrological cycle, directly influencing irrigation planning and crop productivity. However, accurately estimating ET at local scales remains a challenge due to the limitations of conventional measurement methods and the difficulty of integrating high-resolution remote sensing data. This study investigates the estimation of terrestrial evapotranspiration (ET) in a sugarcane cultivation area located in the northern coastal region of Paraíba, Brazil, using meteorological data and aerial images acquired by an Unmanned Aerial Vehicle (UAV). We adapted the PT-JPL model to estimate ET at the local scale, using thermal and multispectral imagery obtained from UAVs. Data validation was performed using surface energy balance measurements obtained from a micrometeorological tower, thereby enabling comparison of estimated and observed ET values. The results demonstrated strong correlations between modeled predictions and field measurements of net radiation (R2 = 0.85), with performance metrics indicating moderate reliability for local-scale simulated ET when compared to flux-tower-based ET (R2 = 0.48; RMSE ≈ 0.045 mm/30 min). This research highlights the potential of integrating UAV-based remote sensing with the PT-JPL model to improve understanding of crop water use, support irrigation management, and contribute to sustainable agricultural practices.

AgriEngineering doi: 10.3390/agriengineering8040148

Authors: Muhammad Irfan Gamal El Afandi

Soil salinity poses a major challenge to agricultural productivity, especially threatening food security in arid and semi-arid areas. Traditional soil reclamation methods, such as leaching, chemical amendments, and drainage engineering, usually need large amounts of water, involve high costs, and can lead to environmental problems. This review compiles existing knowledge on innovative strategies for managing saline soils, focusing on buried interlayer systems that use materials like straw, sand, gravel–sand mixtures, and biochar. These interlayers improve soil hydraulic properties by preventing capillary rise, encouraging salt leaching, and reducing surface salt buildup. Biochar stands out as a particularly useful material because of its stability, large surface area, porosity, and high cation exchange capacity. These features help improve soil structure, increase water retention, and effectively retain sodium. Evidence from lab and field tests shows that buried biochar layers can stop salt from moving upward, aid in desalinating the root zone, and boost crop yields. While straw and sand interlayers show potential in reducing salinity, biochar is noted for its multifunctionality and long-term effectiveness in addressing salinity problems. The success of buried biochar systems depends on several factors, including the properties of the biochar, how much is used, how deep it is buried, and the specific soil and climate conditions. This review highlights how these systems work, compares their performance, and points out research gaps, advocating for their potential as a sustainable, resource-efficient way to manage salinity and improve soil health over the long term. A substantial proportion of the existing evidence is derived from controlled laboratory studies, and the buried biochar layer approach remains an emerging technique that requires further validation under field conditions. Still, significant knowledge gaps persist regarding long-term performance and water-salt dynamics, while site-specific soil variability and scalability challenges may limit the effective implementation of biochar interlayer systems under field conditions.

AgriEngineering doi: 10.3390/agriengineering8040147

Authors: Danco Davcev Slobodan Kalajdziski Ivica Dimitrovski Ivan Kitanovski Kosta Mitreski

Accurate, timely, and resource-efficient decision-making is critical for sustainable precision agriculture. This paper proposes an agentic AI-based Internet of Things (IoT) framework that enables coordinated, closed-loop perception–decision–action processes across heterogeneous sensing and actuation components. The framework models agricultural systems as distributed collections of goal-driven agents responsible for multimodal sensing, uncertainty-aware reasoning, and adaptive decision-making. To provide a structured foundation, the proposed architecture is formalized within a Multi-Agent Partially Observable Markov Decision Process (MPOMDP) perspective, enabling systematic treatment of coordination, uncertainty, and decision policies. The framework integrates multimodal information sources, including vision-based perception and environmental sensing, and defines mechanisms for their fusion and use in system-level decision-making. A proof-of-concept instantiation is presented using publicly available datasets, combining visual perception models and tabular reasoning models within the proposed agentic workflow. The experiments are designed to demonstrate the feasibility, modularity, and coordination capabilities of the framework, rather than to benchmark predictive performance or provide field-validated evaluation. The results illustrate how multimodal information can be integrated to support adaptive and resource-aware decision processes. Finally, the paper discusses key challenges and outlines directions for future work, including real-world deployment, integration with physical actuation systems, and validation under operational conditions.

AgriEngineering doi: 10.3390/agriengineering8040146

Authors: Binita Ghimire Lorena N. Lacerda Thirimachos Bourlai Guoyu Lu

The number of kernels per ear is a key yield parameter that reflects the effects of breeding and agronomic management practices on crop productivity. However, conventional manual counting is labor-intensive, time-consuming, and prone to human error. This study evaluated the performance of six YOLO models, trained from scratch and fine-tuned, alongside a Faster R-CNN model, for automated kernel detection and counting from manually harvested field corn ear images. Model performance was assessed for predicting the yield and harvest index (HI) of field corn under varying nitrogen and irrigation rates. Results show that models trained with fine-tuning consistently outperform those trained from scratch in both accuracy and computational speed. Among all tested YOLO models, YOLOv11x achieved the highest performance, with a precision of 0.978, a recall of 0.968, a latency of 4.8 ms, and a prediction coefficient of determination (R2pred) of 0.858 for the test set and 0.890 for cross-year datasets. The YOLOv8x model ranked second, whereas YOLOv10x was the worst-performing model. Compared to YOLO, Faster R-CNN performed poorly. Yield and HI predictions using YOLOv11x achieved R2 values of 0.881 and 0.758, respectively, and captured treatment effects. Overall, the findings demonstrate that YOLO-based architecture is highly effective for detecting kernels and predicting yield in precision agriculture applications.

AgriEngineering doi: 10.3390/agriengineering8040145

Authors: Zuoxi Zhao Yuanjun Xu Wenqi Zou Shenye Shi Yangfan Luo

To address the problems of uneven stubble height and high missed-cutting rate caused by the insufficient profiling capability of traditional forage harvesters in complex hilly terrain, this paper designs a three-degrees-of-freedom (DOF) profiling header primarily for typical hilly terrain with gentle slopes of 8–15°. Through pitch, roll, and height adjustments, it stably maintains stubble height at 150 mm. Subsequently, geometric analysis and structural optimization achieved kinematic decoupling among all degrees of freedom, thereby overcoming the inherent limitations of the two-DOF header, such as poor adaptability to longitudinal slope and strong adjustment coupling. Three-dimensional modeling was completed in SolidWorks, multibody dynamics simulation was performed in ADAMS, and a profiling control system incorporating a hydraulic system, multi-source sensor fusion, and a fuzzy PID controller was built. The dynamics simulation results show that under the working conditions of 15° longitudinal and 10° transverse slopes, the stubble height error of the header is controlled within 10%, the attitude angle adjustment error is less than 0.5°, and the dynamic response is excellent. Prototype field tests showed that, compared with the two-DOF header, the three-DOF profiling header improved the stubble height stability by about 35%, reduced the missed-cutting rate by about 5%, and increased the operating efficiency by about 15%. No cutting blade contact with the soil occurred, verifying the rationality of the mechanism design and its adaptability to terrain. This study provides an effective technical solution for improving the mechanization level of forage harvesting in hilly and mountainous areas.

AgriEngineering doi: 10.3390/agriengineering8040144

Authors: Marco Torresan Wesley Porter Lavesta C. Hand Walter Scott Monfort Nicola Dal Ferro Hasan Mirzakhaninafchi Glen Rains

Peanut planting presents unique challenges due to the large, fragile, and irregular seed and the sensitivity of seed metering systems to operating conditions. Field experiments were conducted between 2022 and 2025 in Georgia to evaluate how planting speed, row-unit downforce, vacuum setting, and planter platform influence peanut stand establishment, final within-row plant distribution, and yield in single-row planting systems. Trials included speed × downforce evaluations using an electric seed meter and planter-platform × speed × planter-specific vacuum comparisons involving ground-driven, hydraulic-driven, and electric-driven seed meters. Achieved population was determined from post-emergence stand counts, plant distribution was evaluated using emerged-plant position classification relative to theoretical plant spacing, and yield was measured at harvest. Across site years, achieved population patterns were consistently associated with planting speed and vacuum setting, whereas downforce effects were minor and inconsistent within site years. Higher achieved populations were generally obtained at 5 km h−1 and at higher planter-specific vacuum settings, especially for the ground-driven planter. Hydraulic- and electric-driven planter platforms were less sensitive to changes in speed and vacuum and more often maintained acceptable stands at 8 km h−1. Despite large differences in achieved population and plant distribution, peanut yield was often not significantly reduced until stand loss became severe, indicating substantial yield compensation. Spacing uniformity remained poor across all treatments, with skips and long skips common regardless of planter platform. These results indicate that peanut planting performance in current single-row systems is constrained primarily by seed singulation rather than downforce, and that hydraulic- and electric-driven planter platforms improve operational flexibility more consistently than yield.

AgriEngineering doi: 10.3390/agriengineering8040143

Authors: Jason Barnetson Hemant Raj Pandeya Grant Fraser

This study demonstrates a hierarchical spectral modelling approach for predicting pasture nutrition metrics using TabPFN (Tabular Prior-Data Fitted Network), a transformer-based machine learning architecture. In the face of climate variability, aligning stocking rates with pasture resources is crucial for sustainable livestock grazing, requiring accurate assessments of both pasture biomass and nutrient composition. Our research, conducted across diverse growth stages at five tropical and subtropical savanna rangeland properties in Queensland, Australia, with native and introduced C4 grasses, employed a hierarchical sampling and modelling strategy that scales from laboratory spectroscopy to Sentinel-2 satellite predictions via uncrewed aerial vehicle (UAV) hyperspectral imaging. Spectral data were collected from leaf (laboratory spectroscopy) through field (point measurements), UAV hyperspectral imaging, and Sentinel-2 satellite imagery. Traditional laboratory wet chemistry methods determined plant leaf and stem nutrient content, from which crude protein (CP = total nitrogen (TN) × 6.25) and dry matter digestibility (DMD = 88.9–0.779 × acid detergent fibre (ADF)) were derived. TabPFN models were trained at each spatial scale, achieving validation R2 of 0.76 for crude protein at the leaf scale, 0.95 at the UAV scale, and 0.92 at the Sentinel-2 satellite scale. For dry matter digestibility, validation R2 was 0.88 at the UAV scale and 0.73 at the Sentinel-2 scale. A pasture classification masking approach using a deep neural network with 98.6% accuracy (7 classes) was implemented to focus predictions on productive pasture areas, excluding bare soil and woody vegetation. The Sentinel-2 models were trained on 462 samples from 19 site–date combinations across 11 field sites. The TabPFN architecture provided notable advantages over traditional neural networks: no hyperparameter tuning required, faster training, and superior generalisation from limited training samples. These results demonstrate the potential for accurate and efficient prediction and mapping of pasture quality across large areas (100 s–1000 s km2) using freely available satellite imagery and open-source machine learning frameworks.

AgriEngineering doi: 10.3390/agriengineering8040142

Authors: Muhammad Towfiqur Rahman A. S. M. Bakibillah Adib Hossain Ali Ahasan Md. Naimul Basher Kabiratun Ummi Oyshe Asma Mariam

Artificial intelligence (AI)-assisted drone technology in agriculture has transformed productivity and pest control techniques, resulting in novel solutions to modern farming challenges. Drones utilizing sensors, cameras, and AI algorithms can precisely monitor crop health, soil conditions, and insect infestations. Using AI-assisted drones for precision irrigation and yield predictions further improves resource allocation, promotes sustainability, and reduces operating costs. This review examines recent advancements in AI and unmanned aerial vehicles (UAVs) in precision agriculture. Key trends include AI-driven crop disease detection, UAV-enabled multispectral imaging, precision pest management, smart tractors, variable-rate fertilization, and integration with IoT-based decision support systems. This study synthesizes current research to identify technological progress, implementation challenges, scalability barriers, and opportunities for sustainable agricultural transformation. This review of peer-reviewed studies published between 2013 and 2025 uses major scientific databases and predefined inclusion and exclusion criteria covering crop monitoring, precision input application, integrated pest management (IPM), and livestock (especially cattle) monitoring. We describe the platform and payload trade-offs that govern coverage, endurance, and spray quality; the dominant analytics trends, from classical machine learning to deep learning and embedded/edge inference; and the emerging shift from monitoring-only UAV use toward closed-loop decision-making (detection–prediction–intervention). Across the literature, the strongest opportunities lie in robust field validation, multi-modal data fusion (UAV + ground sensors + farm records), and interoperable standards that enable actionable IPM decisions. Key gaps include limited cross-site generalization, scarce reporting of economic indicators (ROI, payback period, and adoption rate), and regulatory and safety barriers for routine autonomous operations. Finally, we present some case studies to emphasize the feasibility and highlight future research directions of AI-assisted drone technology. Through this review, we aim to demonstrate technological advancements, challenges, and future opportunities in AI-assisted drone applications, ultimately advocating for more sustainable and cost-effective farming practices.

AgriEngineering doi: 10.3390/agriengineering8040141

Authors: Alexandre R. Lima Laurence Sautron Aliki Kalamaridou Nathana L. Cristofoli Andreia C. Quintino Renata A. Amaral Jorge A. Saraiva Margarida C. Vieira

This study investigates the valorisation of Salicornia ramosissima agro-industrial by-product by using cellulose nanofibers (CNFs) extracted from this halophyte to reinforce chitosan-based films. The physical, mechanical, and thermal properties of chitosan films containing 0% (control), 1%, and 2% (w/w) CNF were evaluated. Films were produced by solvent casting with glycerol as a plasticiser. At the 2% CNF concentration, films exhibited a reduced moisture content and increased solubility in aqueous solutions. The water vapour transmission rate (WVTR) decreased as CNF content increased under constant humidity but increased at higher temperature and humidity. Control films were more transparent, yet CNF-reinforced films had higher tensile strength and Young’s modulus, reflecting greater stiffness. Maximum elongation at break decreased markedly with the addition of CNFs. SEM revealed that reinforced films had more heterogeneous, rougher surfaces, particularly at 2% CNF. Thermogravimetric analysis showed that 2% CNF adversely affected the thermal stability of the chitosan film. ATR-FTIR spectra indicated that CNF reinforcement protected against UV-induced degradation. Degradability tests in soil and seawater confirmed that the chitosan–CNF mixture preserved degradability, especially at 1% CNF. These findings demonstrate that reinforcing chitosan-based films with CNFs from S. ramosissima can improve functional properties and suggest the potential of this approach for biomaterials development in food packaging applications.

AgriEngineering doi: 10.3390/agriengineering8040140

Authors: Kaushik Luthra Devisree Chukkapalli Bindu Regonda Chris Isbell Akshita Mishra Griffiths Atungulu

There is a growing demand for alternative low cost and sustainable weed management technology suitable for aerobic and organic farming. This study evaluates 915 MHz microwave heating as a potential non-chemical approach for managing weedy rice while assessing its impact on soil physicochemical properties and selected microbial groups. Microwave power levels of 10, 20, and 30 kW were applied to soil at depths of 2.5, 8.9, and 15.2 cm under controlled laboratory conditions. Weed emergence was quantified using the total germinability index (TGI), and soil physicochemical and microbial responses were analyzed in separate experiments. TGI decreased significantly with increasing microwave power and decreasing soil depth, ranging from 0.84 (10 kW at 15.2 cm) to 0 (20 kW at 2.5 cm and 30 kW at 8.9 cm). For 8.9 cm soil depth, energy levels between 176 and 265 kJ/kg resulted in 80–100% emergence suppression, while treatment of 15.2 cm soil at 30 kW for 30 s (188 kJ/kg) reduced TGI by approximately 80% and germination by 64% relative to control. Soil physicochemical properties showed minimal changes, with values remaining within agronomically acceptable ranges. Total bacterial abundance was not significantly affected, whereas ammonia-oxidizing archaea and bacteria were reduced following treatment. These results indicate that microwave heating can effectively suppress weedy rice emergence under controlled conditions, primarily through thermal effects. However, TGI reflects emergence suppression and does not distinguish underlying mechanisms such as lethality, injury, or dormancy. Additionally, limitations including low replication, lack of depth-matched controls, and limited spatial temperature measurements should be considered. Further field-scale studies are needed to validate performance, optimize energy requirements, and assess long-term soil impacts.

AgriEngineering doi: 10.3390/agriengineering8040139

Authors: Gisselle Juri-Morales Claudia Isabel Ochoa-Martínez José Luis Plaza-Dorado

The chili pepper (Capsicum annuum) is among the most widely consumed vegetables worldwide, valued for its sensory and nutritional properties. Nevertheless, it is highly vulnerable to deterioration due to its elevated moisture content. Effective preservation strategies, such as the addition of salt combined with drying, are therefore crucial to maintaining quality and extending shelf life. This study employed a modified Reaction Engineering Approach (REA) to model the drying kinetics and temperature behavior of chili paste under continuous and intermittent conductive hydro-drying conditions. Thirty experiments were conducted considering various salt concentrations (0, 7.5 and 15 g salt/100 g paste), water temperatures in the hydro-dryer, and heating intermittency through on/off cycles. The modified REA model accurately predicted both moisture and temperature profiles, with determination coefficients of 0.9463 and 0.8820, respectively. In addition to direct validation with the complete dataset, cross-validation between cayenne and jalapeño varieties demonstrated the ability of the model to generalize across different formulations and structural characteristics. These results confirm the robustness of the proposed framework and its suitability as a predictive tool for heterogeneous food matrices. Direct and cross-validation confirmed strong predictive performance across all operating conditions and both chili varieties, supporting the use of the modified REA model as a robust tool for representing coupled moisture–temperature dynamics in conductive hydro-drying of semi-solid matrices. Overall, the model provides a reliable platform for analyzing, designing, optimizing, and controlling hydro-drying processes in semi-solid foods, supporting the development of more efficient and sustainable preservation strategies.

AgriEngineering doi: 10.3390/agriengineering8040138

Authors: Qiaonan Yang Yayong Chen Qing Hai Sehar Razzaq Yiming Cui Xingwang Wang Beibei Zhou

The significant differences in insects trapped by pest detection lamps lead to low classification accuracy of existing models for rice pests. To address this issue, this paper proposes a small pest target detection and classification model (ViT-YOLOv5p) by integrating the YOLO backbone and Transformer module. First, the number of training samples is expanded through data augmentation during model training. Furthermore, appropriate noise data are introduced to enhance the robustness and generalization ability of the model. Before detection and classification, image cutting and stitching strategies are adopted to improve the detection accuracy of small objects. The bounding box of the pest is determined by the YOLO backbone, and the corresponding region is fed into the Transformer model to obtain the classification result. Finally, YOLOv5, Faster R-CNN, YOLOv4, and the proposed ViT-YOLOv5p are trained on the same dataset, with average detection time (ADT) and classification accuracy employed as evaluative metrics. The results show that ViT-YOLOv5p achieves the highest classification accuracy of 91.89% with an ADT of 50.41 ms. Compared with the commonly used Faster R-CNN, YOLOv5, and YOLOv4 models, the accuracy is improved by 1.50%, 8.71%, and 9.74%, respectively. This study provides a reference for agricultural pest detection, automatic insect classification systems, and deep learning-based detection of small agricultural targets.

AgriEngineering doi: 10.3390/agriengineering8040137

Authors: Aylla Joani M. de O. Pontes Yone Domingues dos Santos Nascimento Ivan Felipe Silva dos Santos Geraldo Lúcio Tiago Filho Regina Mambeli Barros

Energy recovery from sewage sludge represents a sustainable and technically feasible alternative to promote integration between environmental sanitation and renewable energy generation. This study presents a case analysis of the municipality of Ribeirão Preto, São Paulo, focusing on comparisons between two wastewater treatment systems: an Upflow Anaerobic Sludge Blanket (UASB) reactor and a continuous-flow activated sludge system. Using the UASB configuration, we prepared a preliminary design of a treatment plant based on population and effluent generation projections over a 20-year horizon. The estimated sludge and biogas production allowed us to simulate electricity generation then. The comparative economic assessment, which employed Net Present Value (NPV) and Internal Rate of Return (IRR) indicators in accordance with ANEEL Resolution No. 482/2012, showed that the UASB system yields hard superior methane (up to 3235.6 m3/day) and higher electricity generation potential (1839.7 MWh/year) than the activated sludge system (1990 m3/day and 1654.3 MWh/year, respectively). Both systems were economically viable, with a positive NPV, an IRR of up to 16.83%, and payback periods starting in the first cycle. Furthermore, we estimated the cost per cubic meter of generated biomethane, conducted a sensitivity analysis, and assessed the impact on the most important economic indicators, all to identify the advantages and disadvantages of the proposed project and the best use of the generated biogas. This analysis showed that it is possible to recover energy from sewage treatment systems while also reusing sewage sludge for agricultural applications, thereby highlighting additional environmental and economic benefits, particularly in regions with a strong presence of agribusiness, e.g., Ribeirão Preto.

AgriEngineering doi: 10.3390/agriengineering8040136

Authors: Kaushik Luthra Abhay Markande Josiah Ojeniran Griffiths Atungulu Kuldeep Yadav

Parboiling enhances the nutritional, structural, and economic value of rice, yet its adoption in the United States remains limited despite rising domestic and export demand. This review summarizes key stages of the parboiling process and their effects on milling yield, grain integrity, nutrient retention, and glycemic response. It outlines major industry challenges, including high energy and water use, uneven heating and drying, handling of defective kernels, limited automation in smaller mills, labor shortages, and emerging climate-related risks. Advances such as vacuum soaking, infrared and microwave-assisted drying, smart sensors, and AI-driven control systems show strong potential to improve efficiency and product quality. Circular-economy strategies, including biomass energy recovery, water reuse, and by-product valorization, offer additional sustainability gains. Continued research, modernization, and policy support are critical to strengthen competitiveness and positioning of the U.S. parboiled rice sector for a more resilient and sustainable future.

AgriEngineering doi: 10.3390/agriengineering8040135

Authors: Alana Hellen Batista de Almeida Daniel Viana de Freitas Caio Alisson Diniz da Silva Valdívia Gomes de Sousa Bezerra Ana Candida Lobão da Costa Mateus Alencar Bezerra Silva Francisca Daniele da Silva Jesley Nogueira Bandeira Maria Carolina Ramirez Hernandez Lucrecia Pacheco Batista Matheus de Freitas Souza Frederico Ribeiro do Carmo Paulo Sergio Fernandes das Chagas Bruno Caio Chaves Fernandes Daniel Valadão Silva

Triazine herbicides are widely used for weed control in agricultural systems, and their occurrence in water bodies has been frequently reported worldwide. This study assessed the efficiency of a hydrochar derived from the epicarp and mesocarp of passion fruit residues for the removal of three triazine herbicides (atrazine, ametryn, and metribuzin), with the aim of developing a material suitable for application in water remediation programs. The adsorption capacity of biomass and hydrochar derived from passion fruit residues was evaluated with and without activation using 0.5 mol L−1 phosphoric acid. The adsorption of herbicides was not significantly affected by pH within the range of 4 to 8. The acid hydrochar, which exhibited the highest removal capacity among the evaluated adsorbents, presented adsorption capacities of 18.05, 10.83, and 5.05 µg g−1 for atrazine, ametryn, and metribuzin, respectively. These values correspond to removal efficiencies of approximately 62%, 72%, and 52% at initial concentrations of 0.33, 0.25, and 0.15 mg L−1. The adsorption equilibrium time varied among the herbicides, reaching 4 h for atrazine and ametryn and 5 h for metribuzin. The adsorption dynamics between the adsorbents and adsorbates were best described by the pseudo-second-order kinetic model for ametryn and metribuzin, while atrazine had a higher correlation with the Elovich equation. The Weber–Morris model did not adequately describe the adsorption process. Among the isotherms tested, the Freundlich model provided the best fit for all three herbicides. The desorption rates of the acid hydrochar were 51%, 13%, and 83% for atrazine, ametryn, and metribuzin, respectively. Therefore, hydrochar derived from passion fruit residues represents a promising alternative for the remediation of triazine herbicides.

AgriEngineering doi: 10.3390/agriengineering8040134

Authors: Kanokporn Promnikorn Thanpitcha Jenkit Piya Kittipadakul Ekaphan Kraichak

Leaf Area Index (LAI) is critical for monitoring cassava growth and yield prediction, yet ground measurements are time-consuming and labor-intensive for large-scale applications. While satellite-based vegetation indices (VIs) offer a scalable alternative, their performance for cassava LAI estimation remains poorly documented, and optimal index selection for different growth stages is unclear. This study evaluated the predictive performance of 13 Sentinel-2-derived VIs for estimating ground-measured LAI across cassava growth stages. Ground-LAI was measured monthly using a SunScan Canopy Analyzer from January to June 2022 (2–7 months after planting; MAP) in 47 cassava plots in Nakhon Ratchasima Province, Thailand. Linear mixed-effects models and stage-specific regressions assessed VI predictive performance using Coefficient of determination (R2) and Root Mean Squared Error (RMSE). The Green Normalized Difference Vegetation Index (GNDVI) and Normalized Difference Water Index (NDWI) demonstrated superior performance across all growth stages (R2 = 0.524; RMSE = 0.350), followed by Sentinel-2 LAI Green Index (SeLI R2 = 0.521, RMSE = 0.357). Stage-specific analysis revealed that Ratio Vegetation Index performed best during early growth (2 MAP, R2 = 0.671; RMSE = 0.164) while GNDVI and NDWI excelled during mid-growth (3–5 MAP) and SeLI at late growth (7 MAP, R2 = 0.393; RMSE = 0.422). While the presence of large trees altered the ranking of VI predictive performance, it did not substantially affect estimation errors, suggesting a relatively small impact of spatial heterogeneity on LAI estimation accuracy. These findings identify GNDVI and NDWI as the most operationally suitable Sentinel-2 indices for cassava LAI estimation and demonstrate that stage-specific index selection can improve monitoring accuracy, providing validated tools for regional-scale cassava crop monitoring using freely available satellite data.

AgriEngineering doi: 10.3390/agriengineering8040133

Authors: Vitali Czymmek Jost Völckner Felix Zilske Stephan Hussmann

Weed management, particularly in organic farming, poses a significant challenge due to high manual labor costs and the crop’s low competitive ability. Precision laser technology offers a promising non-chemical alternative. This study evaluates the field performance of a novel robotic system based on a Thulium fiber laser. The validation was conducted on commercial fields of the Westhof Bio GmbH in Friedrichsgabekoog, Germany. The Weeding Success rate of the laser weeding robot was 95% and the Detection Rate 85% for carrots for one weeding cycle. For beetroot, these values are 98% and 88%, respectively, after two weeding cycles. The field trials validate the Thulium fiber laser system as an agronomically effective and economically viable alternative for sustainable weed management. The technology demonstrates the potential to significantly reduce manual labor and reliance on herbicides in challenging crops.

AgriEngineering doi: 10.3390/agriengineering8040132

Authors: Georgios Bourodimos Michael Koutsiaras Vasilis Psiroukis Aikaterini Kasimati Spyros Fountas

Spray drift is one of the most significant challenges in the application of Plant Protection Products (PPPs), as it contributes to water, soil, and food contamination and is highly associated with health risks to agricultural workers, bystanders, and rural residents. Spray drift is defined as the fraction of PPP that is carried away from the target area by air currents during application. Factors such as high wind speeds, low relative humidity, and elevated temperatures increase the risk of drift by promoting droplet evaporation and off-target movement. Technological advancements in spraying equipment, such as low-drift and air induction nozzles, have been shown to significantly reduce drift potential. Air induction nozzles mix air with the spray liquid, creating larger droplets that are less susceptible to drift. The primary objective of this study was to quantify the spray drift reduction achieved using cost-effective and easily applicable drift mitigation techniques that do not require specialized and expensive equipment compared to conventional application methods in vineyards under Southern European conditions. Field measurements followed the ISO 22866:2005 protocol, using a conventional axial fan air-assisted sprayer that is commonly used by vineyard farmers in Greece. This study was conducted on Savatiano vines, the most widely cultivated winemaking variety in the Attica region, characterized by its low height. The spraying techniques evaluated as spray drift mitigation measures were one-sided spraying applications of the outer vineyard row; one-sided spraying applications of the two last rows; spraying with closed air assistance on the outer rows; and finally, spraying with the use of air induction nozzles. Results indicated that each technique produced varying amounts of sedimenting drift over distance. Spraying without air assistance consistently generated the lowest levels of drift at almost all distances. While air induction nozzles initially increased drift deposition within the first 4 m, they significantly reduced drift beyond 5 m. These findings demonstrate that simple operational adjustments to conventional vineyard sprayers, particularly reducing or switching off air assistance in outer rows, can substantially decrease spray drift without requiring additional investment in specialized equipment. Overall, spraying without air support achieved the greatest drift reduction across all distances from the vineyard, followed by air induction nozzles, which were equally effective at further distances (past 5 m) but less so near the application area. The results provide practical guidance for vineyard growers seeking low-cost strategies to minimize agricultural input losses, environmental contamination, and improve the sustainability of pesticide applications.

AgriEngineering doi: 10.3390/agriengineering8040131

Authors: Fábio Henrique Rojo Baio Paulo Eduardo Teodoro Job Teixeira de Oliveira Ricardo Gava Larissa Pereira Ribeiro Teodoro Cid Naudi Silva Campos Estêvão Vicari Mellis Isabella Clerici de Maria Marcos Eduardo Miranda Alves Fernanda Ganassim João Pablo Silva Weigert Kelver Pupim Filho Murilo Bittarello Nichele João Lucas Gouveia de Oliveira

Maize cultivation in tropical Oxisols during the second growing season faces significant climatic risks, where spatial heterogeneity in soil water retention often dictates economic viability. This study integrated a trimodal sensing approach, combining multispectral, thermal, and LiDAR data, with proximal physiological measurements to map isohydric responses and yield variability. Conducted in the Brazilian Cerrado, the research monitored a one-hectare maize field using UAV-based sensors alongside ground truth evaluations of gas exchange, leaf water potential, and soil moisture. Results revealed high yield variability (6.6 to 13.4 Mg ha−1) primarily governed by clay content-mediated water availability. Maize exhibited strict isohydric behavior, maintaining homeostatic leaf water potential through preventive stomatal closure, which limited CO2 assimilation in zones with lower water retention. A significant statistical decoupling was observed between plant height and final grain yield, as water stress impacted reproductive stages more severely than vegetative growth. Furthermore, the Temperature Vegetation Dryness Index (TVDI) served as a robust proxy for biomass vigor rather than mere water deficit. These results confirm that yield variability in tropical Oxisols was not a product of hydraulic failure, but rather a consequence of carbon limitation necessitated by the crop’s conservative hydraulic management to maintain leaf water potential within safe thresholds.

AgriEngineering doi: 10.3390/agriengineering8040130

Authors: Atsushi Ogawa Masaru Terakado Ryoei Nakadate Rento Chiba Nana Yamamoto

Effective post-harvest management of avocados is essential for reducing supply chain losses. This requires an accessible, cost-effective method for accurately predicting ripeness under real-world conditions. This study developed a non-destructive framework for predicting avocado ripeness using portable visible–near-infrared (Vis-NIR) spectrometers and analyzed the storage temperature dependencies. A 10-point sensory-based ripeness index was correlated with second-derivative reflectance spectra using partial least squares (PLS) regression. To ensure model robustness, we employed repeated 10-fold cross-validation. The broadband PLS model achieved a residual predictive deviation (RPD) of 1.36, while a simplified model using six specific wavelengths (570, 977, 1120, 1161, 1398, and 1655 nm) demonstrated an RPD of 1.43, confirming its feasibility as a preliminary screening tool. Key wavelengths identified were associated with chlorophyll degradation and lipid accumulation. Furthermore, a significant logarithmic relationship (r = 0.9965) was observed between storage temperature (15–35 °C) and the daily ripening rate. Our results suggest that ripening progression is significantly suppressed at temperatures of approximately 12 °C or below. These findings provide quantitative guidelines for distributors to optimize logistics and shelf-life management using portable technology, contributing to the digitalization of consumer-aligned ripeness assessment.

AgriEngineering doi: 10.3390/agriengineering8040129

Authors: Adam Y. Whitfield Jeremy K. Greene Kendall Kirk Curtis Erwin Francis P. F. Reay-Jones Michael Plumblee

Insect pests are a major limiting factor to producing profitable soybean (Glycine max (L.) Merr.) in South Carolina. Production practices within the soybean industry have drastically evolved over the last few decades, but treatment thresholds for insect pests have stayed the same. Evaluating treatment thresholds for insect pests typically involves simulating injury because it offers a controlled and repeatable way to evaluate an injury–yield relationship. Simulating defoliation injury in soybean typically involves methods such as hand-plucking or cutting leaflets, but these methods are not truly representative of insect feeding injury. This study describes the design, development, and validation of a novel pneumatic leaf puncher created with a 3D printer and used to simulate insect defoliation injury in soybean. The device was engineered to deliver controlled, repeatable leaf tissue removal at varying target levels (5, 15, 30, and 40%) by using interchangeable punching plates. Simulated defoliation treatments were applied to mature leaves on soybean plants at the V6 growth stage in a greenhouse study. The leaf area removed was quantified using LeafByte, a mobile app designed for measuring leaf area, and confirmed against target values. Results showed a high level of correlation between intended and actual defoliation levels, with accuracy ≥ 90%. The pneumatic leaf puncher provides a potential standardized method for administering foliar damage and offers a reliable alternative to manual clipping or herbivory feeding trials in defoliation research. Ongoing field trials at Clemson University will incorporate yield data to refine defoliation thresholds. Due to its adaptability and ease of use, the pneumatic leaf puncher could be implemented regionally, nationally, or internationally to support standardized defoliation studies across diverse cropping systems.

AgriEngineering doi: 10.3390/agriengineering8040128

Authors: Youssef Mouzouna Ayman Khafif Mohammed El Mahfoud Hanane Nasraoui Najib El Ouanjli Abdelhadi Ennajih

The early and accurate detection of plant diseases is essential for crop management and agricultural loss control, especially under resource limitations. We propose an optimized YOLO11n architecture, designated as YOLO11_Opt, targeting real-time inference on low-cost embedded systems. The model is computationally efficient through the selective narrowing of its width and depth, while performing competitively in two-class object recognition tasks. Pepper leaves were chosen as the materials for study. Three methods of quantization (FP32, FP16, and INT8) were investigated. After running the experiments, the results showed that YOLO11_Opt greatly reduces the computational complexity: the complexity decreased from 6.3 GFLOPS and 2.58 million parameters in the typical YOLO11n model to a very small 0.5 GFLOPS and 0.33 million parameters, while maintaining competitive detection capabilities. The improved FP32 model has a mAP (0.5:0.95) of 0.913 and a precision of 0.991, while the old version has 0.961 mAP and 0.996 precision. Lastly, implementations on embedded hardware prove that the method is feasible: the detection accuracy of the system in live classification is around 92% with Raspberry Pi 4 and 94% with NVIDIA Jetson Nano, with inference times of as little as 1.9 ms on NVIDIA Jetson Nano and 8.3 ms on Raspberry Pi 4. Thus, YOLO11_Opt demonstrates significant potential as a reliable, high-performance, low-cost solution to identifying plant diseases on devices in precision agriculture.

AgriEngineering doi: 10.3390/agriengineering8040126

Authors: Fernanda Miranda Zoppas Tatiane Benvenuti Daiana Maffessoni

The wine industry generates large quantities of grape pomace (GP), a lignocellulosic by-product rich in fibers, polyphenols, lipids, and minerals. Improper management and disposal of GP can lead to significant environmental impacts, whereas its valorization creates significant opportunities within a circular economy framework. This review examines the conversion of GP from an agro-industrial residue into functional materials for water and wastewater treatment. Recent advances in GP characterization, thermochemical conversion into biochars, development of hybrid silica- and biopolymer-based composites, and the use of polyphenol-rich extracts for green synthesis of nanomaterials are critically reviewed. GP-derived materials have exhibited high removal efficiencies for dyes, heavy metals, and emerging contaminants, while hybrid systems improve stability, selectivity, and catalytic performance. Despite promising laboratory-scale results, major challenges remain regarding regeneration efficiency, long-term stability, and scalability, which currently limit the competitiveness of GP-derived materials compared to commercial adsorbents. Furthermore, the lack of comprehensive life cycle assessment and techno-economic analysis hinders the validation of their environmental and economic viability, underscoring the need for integrated assessments to guide sustainable implementation. Overall, GP is positioned as a second-generation residue with strong potential for cascading valorization strategies that integrate high-value compound recovery with environmental applications, supporting the development of sustainable water purification technologies.

AgriEngineering doi: 10.3390/agriengineering8040127

Authors: Marco Torresan Wesley Porter Lavesta Camp Hand Walter Scott Monfort Nicola Dal Ferro Hasan Mirzakhaninafchi Glen Rains

Cotton planting efficiency is increasingly constrained by narrow planting windows, motivating interest in higher operating speeds if stand establishment and seed placement accuracy can be maintained. Field experiments were conducted in Georgia between 2020 and 2025 to quantify the effects of planter operating parameters and system configurations on cotton planter performance. Trials evaluated combinations of planting speed, row-unit downforce, seed plate type (singulated vs. hill-drop), and seed delivery system using conventional gravity-tube planters and two high-speed planter systems equipped with advanced delivery systems. The achieved population was determined from stand counts, planting quality was assessed using plant position classification relative to theoretical plant spacing, and lint yield was measured at harvest. Across site-years, the achieved population was generally not affected by planting speed or downforce within the tested ranges. With conventional gravity-tube delivery systems, the proportion of perfectly spaced plants declined from 44.0% to 22.1% in 2020 and from 52.8% to 28.4% in 2021 as planting speed increased from 5 to 11 km h−1. In contrast, across the advanced planter systems evaluated in 2025, mean perfect spacing remained within a narrow range of 45.8% to 49.5% across 8 to 14 km h−1. Hill-drop seed plates increased the achieved population relative to singulated plates in the seed plate × downforce trials, increasing mean achieved population from 79.6 to 87.8 thousand plants ha−1 at Midville and from 62.2 to 73.1 thousand plants ha−1 at Plains in 2022, and from 45.4 to 58.1 thousand plants ha−1 at Midville in 2024, but these increases did not result in consistent lint yield differences. The high-speed hill-drop configuration evaluated in 2025 did not consistently produce plant pairs meeting the hill-drop spacing criterion. These results indicate that current high-speed planter systems can be used for singulated cotton to increase planting productivity while maintaining placement accuracy, although additional research is needed to determine the environmental and management conditions under which spacing improvements translate into yield benefits.

AgriEngineering doi: 10.3390/agriengineering8040125

Authors: Daniel Henrique Leite Domingos Sárvio Magalhães Valente Pedro Maya Ferreira Arruda Gabriel Dumbá Monteiro de Castro Daniel Marçal de Queiroz Diego Bedin Marin Fábio Daniel Tancredi

Coffee is among the primary agricultural commodities in international trade; however, mapping coffee crops in mountainous regions faces limitations due to high spectral variability and complex canopy structures. This study hypothesized that optimized spectral band combinations focused on the visible spectrum may outperform configurations including near-infrared (NIR) for coffee crop segmentation. This work aimed to evaluate how different spectral band combinations affect the performance of the U-Net for segmenting coffee crops in mountainous regions. Seven PlanetScope images (4 m resolution) from Matas de Minas, Brazil, covering different phenological stages in 2023–2024, were divided into 316 training patches and 25 test patches of 256 × 256 pixels and used to train U-Net models across five spectral band combinations: (B, G, R), (B, G, NIR), (B, R, NIR), (G, R, NIR), and (B, G, R, NIR). The visible spectrum combination (B, G, R) demonstrated superior performance with an overall Accuracy of 0.8669 and, for the Coffee Crops class, an F1-score of 0.8682 and an IoU of 0.7671, outperforming all NIR-inclusive configurations. Visible bands’ sensitivity to pigmentation variations proved more effective in heterogeneous environments, while NIR increased spectral confusion near native vegetation and crop edges. The model overestimated cultivated area by 18.3% due to mixed pixels from 4 m resolution and mountainous terrain. These findings confirm that visible-spectrum bands offer a cost-effective alternative for coffee segmentation, though higher spatial resolution is needed for improved boundary delineation.

AgriEngineering doi: 10.3390/agriengineering8040124

Authors: Túlio Souza Mariano Carlos Eduardo Alves Oliveira Flávio Justino Fernanda Campos de Sousa Charles Paranhos Oliveira Ilda de Fátima Ferreira Tinôco Luciano Barreto-Mendes Gabriela Mariano Ismael de Oliveira Cavalcante Matteo Barbari

Heat stress is one of the main factors limiting the efficiency and sustainability of dairy production in tropical and subtropical regions, impairing animal welfare, as well as productive and reproductive performance. Among mitigation technologies, evaporative cooling stands out as a widely adopted strategy in intensive production systems. However, there are still no consolidated metrics capable of quantifying, in a comparative and regionalized manner, the theoretical potential for using this technique under different climatic conditions. In this context, the objective of this study was to develop an innovative modeling-based metric to estimate the potential for evaporative cooling based on thermal environment indices and heat transfer principles. Sixteen-year time series of hourly meteorological data from three Brazilian municipalities located in major dairy-producing regions [Uberlândia (MG), Luziânia (GO), and Uruguaiana (RS)], encompassing representative tropical and subtropical conditions, were used to calculate the Temperature–Humidity Index (THI) and to develop the metric termed Radiant Heat Load Reduction under Evaporative Cooling (ΔRHL). Based on the results obtained, THI values were found to frequently exceed the thermal comfort thresholds between September and April, corresponding to the warmest period of the year in Brazil. However, the relationship between the intensity of heat stress and the theoretical potential for evaporative cooling varied significantly among the locations, indicating that the potential efficiency of evaporative cooling is strongly dependent on regional climatic conditions and is more consistent in subtropical environments. It is concluded that the proposed metric constitutes an innovative tool for the spatial and temporal quantification of the potential efficiency of evaporative cooling, with applicability in decision support and in the planning of thermal mitigation strategies in intensive dairy production systems.

AgriEngineering doi: 10.3390/agriengineering8040123

Authors: Darat Dechampai Sasissorn Kasemsuksirikul Supitchaya Promsuwan Punyaporn Larfon

This study provides a practical and replicable improvement model for productivity and inspection reliability improvement in resource-constrained food logistics environments. This study presents an engineering-based optimization of productivity and process reliability in an agro-industrial post-harvest processing system for canned young green jackfruit using an integrated Define–Measure–Analyze–Improve–Control (DMAIC) and Failure Mode and Effects Analysis (FMEA) framework. The case-study production system experienced high raw-material loss, prolonged blanching cycles, and low inter-operator inspection agreement, which reduced process yield and logistics throughput. Root causes were identified through process mapping and fishbone analysis and prioritized using FMEA Risk Priority Number (RPN) scoring. Key improvement actions included optimizing blanching time, standardizing supplier grading to reduce material variability, and strengthening inspection decisions through Attribute Gage Repeatability and Reproducibility (Gage R&R)-based training and criteria alignment. After implementation, productivity increased by 2.31%, raw-material loss decreased by 1.90%, and inter-operator inspection agreement improved by 16%, exceeding the benchmark. Blanching time was reduced from 3 to 1 min at ≥90 °C, shortening cycle time by 67% and generating an estimated annual cost saving of USD 7200 without major capital investment. The results demonstrate that structured, risk-based improvement combined with validated measurement systems can enhance workforce consistency, process stability, and logistics flow efficiency in agro-industrial food processing environments, providing a replicable improvement model for agro-industrial processing small and medium-sized enterprises (SMEs).

AgriEngineering doi: 10.3390/agriengineering8040122

Authors: Maramreddy Srinivasulu Sandipan Maiti

Early and precise detection of plant diseases is essential for safeguarding crop yield and ensuring sustainable agricultural practices. In this study, we propose the Modified RefineNet with Attention based Fusion (MoRefNet-AF), a Modified RefineNet architecture enhanced with attention-based fusion for multi-class classification of corn (maize) and Pepper leaf diseases. Unlike the original RefineNet, which was segmentation-oriented and computationally heavy, MoRefNet-AF is redesigned for lightweight and discriminative classification. The modifications include replacing standard convolutions with depthwise separable convolutions for efficiency, adopting the Mish activation function for smoother gradient flow, redesigning the multi-resolution fusion module with concatenation and shared convolution for richer cross-scale integration, and incorporating Squeeze-and-Excitation (SE) blocks for adaptive channel recalibration. Additionally, Chained Residual Pooling (CRP) with atrous convolutions enhances contextual representation, while global average pooling with dense layers improves classification readiness. When evaluated on a curated six-class dataset combining PlantVillage and Mendeley leaf disease repositories, MoRefNet-AF achieved 99.88% accuracy, 99.74% precision, 99.73% recall, 99.95% F1-score, and 99.73% specificity. These results outperform strong baselines including ResNet152V2, DenseNet201, EfficientNet-B0, and ConvNeXt-Tiny, while maintaining only 0.3 M parameters. With its compact design and TensorFlow Lite (v2.13) compatibility, MoRefNet-AF offers a robust, lightweight, and real-time deployable solution for precision agriculture and smart plant disease monitoring.

AgriEngineering doi: 10.3390/agriengineering8040121

Authors: Jaya Shankar Tumuluru Paul A. Funk Ronald P. Haff Andrew Paul Breksa Joseph S. McIntyre Kathleen M. Yeater Derek P. Whitelock Carlos B. Armijo Yuzhu Zhang Wally Yokoyama

Research is needed to improve walnut drying throughput and energy consumption in hulling plants, but current methods for sampling nuts in commercial drying bins and measuring nut moisture content limit the capacity to investigate the drying process thoroughly. A novel apparatus for obtaining walnut samples at multiple depths and locations in stadium drying bins and a novel rapid method for accurately determining walnut in-shell moisture content were developed. A second rapid moisture measurement method involving near-infrared light (NIR) was also investigated. The sampling apparatus consisted of three sampling columns installed in each walnut drying bin. Each column had gate valves at four elevations, admitting approximately 30 in-shell walnuts to rectangular buckets hanging on a cable just below each gate valve. To collect samples, the gates were opened and closed, the buckets were withdrawn, the nut samples were collected and sealed in labeled bags, and then the buckets were returned to the column to be ready for the next sampling interval. This configuration, sampling nuts at four levels across three locations in the drying bin, allowed better moisture content variability investigation during in-bin walnut drying. The rapid moisture content measurement method consisted of selecting twelve representative in-shell walnuts from each sample and grinding them in a mill. Twelve grams were sub-sampled from the well-mixed ground material and dried in an oven at 105 ± 1 °C for 3 h, then reweighed to determine moisture loss. The coefficient of variation for sub-samples within an individual sample (n = 4) averaged 2.65% for moisture contents ranging from 6% to 47% dry basis. The rapid moisture content measurement method reduced the drying time from 24 h to 3 h compared to conventional oven drying method, with an accuracy of ±0.5 to 1.5% of the full moisture content range. The best correlation observed between the NIR methodology and the rapid moisture content method was 0.74 R2. These new in-bin walnut sampling and moisture-content measurement methods will accelerate future research aimed at improving walnut drying at commercial huller facilities.

AgriEngineering doi: 10.3390/agriengineering8040120

Authors: Svetlana Yu. Khashirova Albert S. Shabaev Igor F. Turkanov Elena V. Bondarchuk Valery G. Gryaznov Ekaterina A. Galkina Polina N. Bolotskova Irina M. Kaigorodova Elena G. Kozar Vladimir G. Zainullin

Electromagnetic treatment (EMF) can stimulate seed germination and plant development, including mitigating the negative effects of stressors. One non-invasive approach to detecting the early effects of EMF exposure is the study of gas exchange dynamics during the seed imbibition stage. Gas chromatography was used to assess the effect of low-intensity non-thermal EMF on the concentration of H2, O2, CO2, and NH3 gases in the “soil–pea seed” system under optimal conditions and under salt stress. EMF treatment exhibited a variant-dependent effect. Under optimal conditions, it stimulated respiration (O2 concentration decreased by 12%, CO2 increased by 15%); under salinity, the concentration of both gases decreased by 8–10% relative to the control. H2 emission proved to be a sensitive biochemical marker of the response to external factors. Under optimal conditions, EMF treatment nearly tripled H2 emission and shifted its emission peak one day earlier, which may indicate accelerated mobilization of the seed’s defense systems under developing hypoxia. Salinity reduced H2 levels by an order of magnitude, while EMF treatment stabilized the H2 emission rate, reducing it by almost half. Thus, EMF should be regarded as a modifier of the seed’s metabolic response to imbibition conditions, rather than solely as a germination stimulant.

AgriEngineering doi: 10.3390/agriengineering8030119

Authors: Sandra Skendžić Darija Lemić Hrvoje Novak Marko Reljić Marko Maričević Vinko Lešić Ivana Pajač Živković Monika Zovko

Assessing crop water status during the reproductive development of winter wheat is challenging because soil–plant–atmosphere interactions are strongly influenced by soil physical conditions, and measured soil water content (SWC) does not necessarily reflect plant-accessible water. This study applied an integrated, process-based multisensor approach to evaluate functional crop water status and its relationship to grain yield, combining hyperspectral canopy reflectance, atmospheric observations, in situ SWC, and pedological characterization. Five winter wheat cultivars were monitored at two contrasting pedoclimatic sites in continental Croatia during the 2022/2023 growing season. Hyperspectral canopy reflectance (350–2500 nm) was measured at reproductive stages (BBCH 61–83), and seventeen vegetation indices describing canopy water status, structure, pigments, and senescence were derived. Principal component analysis (PCA) identified location as the dominant source of spectral variability, while cultivar effects were secondary. Although atmospheric conditions were broadly comparable, the sites differed markedly in soil physical properties, resulting in contrasting soil water–air regimes. Despite consistently higher volumetric SWC at one site, hyperspectral indicators revealed lower canopy water status, reduced canopy structure, earlier senescence, and lower grain yield across all cultivars. Water-sensitive indices exploiting near-infrared (700–1300 nm) and shortwave infrared (1300–2400 nm) bands (NDWI, NDMI, NMDI, MSI) consistently indicated greater physiological stress. Conversely, the site with lower SWC but more favorable soil physical conditions exhibited higher values of water- and structure-related indices and achieved higher grain yield, with a mean increase of 669 kg ha−1. The results demonstrate that hyperspectral canopy reflectance captures yield-relevant water stress that cannot be inferred from soil moisture alone, highlighting the importance of multisensor integration for interpreting soil–plant–atmosphere interactions under heterogeneous soil conditions.

AgriEngineering doi: 10.3390/agriengineering8030118

Authors: Thallyta das Graças Espíndola da Silva Diogo Paes da Costa Rafaela Félix da França Argemiro Pereira Martins Filho Maria Renaí Ferreira Barbosa Jamilly Alves de Barros Gustavo Pereira Duda Claude Hammecker José Romualdo de Sousa Lima Ademir Sérgio Ferreira de Araújo Erika Valente de Medeiros

Data-driven approaches are increasingly required to optimize biofertilization strategies in forage systems. Machine learning (ML) provides an efficient tool for identifying functional drivers in complex plant–soil–microbe systems, offering important perspectives for precision data-driven agriculture. However, despite its potential, ML remains data-driven in studies involving diazotrophic inoculation using biochar as a pelletizing material, particularly in forage grasses. This study applied ML to predict the key drivers controlling Brachiaria brizantha performance and soil quality under biochar-pelletized diazotrophic bacteria (DB). Five isolates were inoculated with or without biochar, and plant traits and soil attributes, including pH, potassium, phosphorus, sodium, and urease activity were evaluated. These data were integrated into multivariate analyses and ML algorithms, including Linear Discriminant Analysis, Random Forest, and Support Vector Machine, to identify the functional drivers that best discriminate treatment performance and uncover mechanistic functional drivers. All isolates increased soil potassium content, with the highest values in the biochar amended treatments, and a 39% increase. Soil pH and urease activity were significantly modulated by isolate identity, while biomass allocation patterns differed among treatments. Overall, the results highlight that biochar pelletization can enhance the effectiveness of DB inoculants. ML revealed that dry foliar biomass, soil pH, and fresh root weight were the most predictive variables, highlighting consistent signatures explaining plant–soil responses to biochar-pelletized DB. These findings demonstrate that interpretable ML can disentangle complex plant–soil–microbe interactions, support precision biofertilization design, and serve as an efficient decision-support tool for sustainable pasture management. Beyond the present system, this study establishes a transferable and scalable analytical framework for precision biofertilization strategies in forage systems and other biochar-mediated agroecosystems, advancing predictive and data-driven approaches in sustainable agricultural engineering.

AgriEngineering doi: 10.3390/agriengineering8030117

Authors: Wawan Hermawan Radite Praeko Agus Setiawan Diang Sagita Reka Ardi Prayoga

Feed handling activities in cattle feedlots—such as feed leveling, reconditioning, and the removal of feed refusals along the trough—remain largely manual in many developing countries, resulting in high labor demand and inconsistent feed availability. This study aimed to design, develop, and evaluate an integrated machine capable of leveling feed, conditioning refused feed, and collecting feed refusals. The machine was developed using trough-geometry data (550–650 mm) and feed-residue properties (particle size, bulk density, terminal velocity), integrating a shovel–dual-brush unit with pneumatic suction. A prototype was subsequently fabricated and tested under practical feedlot conditions using various trough widths and operating speeds. Performance evaluation included feed-pile geometry, distribution uniformity, suction efficiency, suction capacity, and fuel consumption. The leveling mechanism significantly improved feed distribution uniformity, reducing the coefficient of variation for feed-pile height and feed mass by up to 67% and 73%, respectively. During conditioning, the machine increased feed-pile height by 8–10 cm and reduced pile width by 6–13 cm. The suction system maintained high efficiency (94–97%) with an average capacity of 14.1 ± 0.8 kg·min−1. Fuel consumption ranged from 0.54 L·h−1 during leveling to 1.30 L·h−1 during suction. Overall, the machine offers a practical solution for improving feed shaping, uniformity, and residue removal.

AgriEngineering doi: 10.3390/agriengineering8030116

Authors: Kaixuan Fang Yuzhen Lu Xinyang Mu

Traditional mechanized chestnut harvesting is too costly for small producers, non-selective, and prone to damaging nuts. Accurate, reliable detection of chestnuts on the orchard floor is crucial for developing low-cost, vision-guided automated harvesting technology. However, developing a reliable chestnut detection system faces challenges in complex environments with shading, varying natural light conditions, and interference from weeds, fallen leaves, stones, and other foreign on-ground objects, which have remained unaddressed. This study collected 319 images of chestnuts on the orchard floor, containing 6524 annotated chestnuts. A comprehensive set of 29 state-of-the-art real-time object detectors, including 14 in the YOLO (v11–v13) and 15 in the RT-DETR (v1–v4) families at various model scales, was systematically evaluated through replicated modeling experiments for chestnut detection. Experimental results show that the YOLOv12m model achieved the best mAP@0.5 of 95.1% among all the evaluated models, while RT-DETRv2-R101 was the most accurate variant among the RT-DETR models, with mAP@0.5 of 91.1%. In terms of mAP@[0.5:0.95], the YOLOv11x model achieved the best accuracy of 80.1%. All models demonstrated significant potential for real-time chestnut detection, and YOLO models outperformed RT-DETR models in terms of both detection accuracy and inference, making them better suited for on-board deployment. This work lays a foundation for developing AI-based, vision-guided intelligent chestnut harvest systems.

AgriEngineering doi: 10.3390/agriengineering8030115

Authors: Romulus Iagăru Pompilica Iagăru Ioana Mădălina Petre Mircea Boșcoianu Sebastian Pop

The proposed study investigates the key factors influencing UAV adoption and proposes an integrated educational–operational framework to enhance implementation in agricultural practice. A case study in Sibiu County, Romania, combined survey-based empirical analysis (n = 80), strategic environmental assessment and the deployment of a demonstration aerial crop monitoring system for educational purposes (ACMS-E). We integrated the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB) to examine adoption intentions, revealing perceived usefulness (β = 0.355, p = 0.021) and positive attitudes (β = 0.382, p = 0.005) as the strongest predictors, explaining 44.1% of variance. Based on these findings, a modular training curriculum was designed, combining theoretical instruction, flight operation exercises, remote sensing techniques, data analytics and farm-management integration. ACMS-E provides hands-on training and promotes capacity-building, bridging the gap between technological availability and real-world adoption. By linking technological capabilities with structured training, ACMS-E bridges the gap between UAV availability and effective implementation, offering a scalable model for precision agriculture. This framework provides a pathway to accelerate UAV adoption, optimize field-level monitoring and support evidence-based, resource-efficient farm management in emerging and developed agricultural contexts.

AgriEngineering doi: 10.3390/agriengineering8030114

Authors: Marcus Vinicius Leite Jair Minoro Abe Irenilza de Alencar Nääs Marcos Leandro Hoffmann Souza

Driven by the global rise in animal protein demand, poultry farming has evolved into a highly intensive and technically complex sector. According to the FAO, animal protein production increased by about 16% in the past decade, with poultry alone expanding by 27% and becoming the leading source of animal protein. This intensification requires rapid, complex decisions across multiple aspects of production under uncertainty and strict time constraints. This study presents the development and evaluation of a conversational decision support system (DSS) designed to support decision-making to assist poultry producers, particularly broiler producers, in addressing technical queries across five key domains: environmental control, nutrition, health, husbandry, and animal welfare. As a proof-of-concept study, the reference context is intensive broiler production, covering common floor-rearing housing settings, including environmentally controlled and mechanically ventilated houses. The system combines a large language model (LLM) with retrieval-based generation (RAG) to ground responses in a curated corpus of scientific and technical literature. Additionally, it adds a reasoning component using Paraconsistent Annotated Evidential Logic Eτ, a non-classical logic designed to handle contradictory or incomplete information. Methodologically, Logic Eτ is used as a workflow-level control mechanism to gate clarification, domain routing, and answer adequacy signaling, rather than serving only as a post hoc label on generated outputs. Evaluation was conducted by comparing system responses with expert reference answers using semantic similarity (cosine similarity with SBERT embeddings). The results indicate that the system successfully retrieves and composes relevant content, while the paraconsistent inference layer makes results easier to interpret and more reliable in the presence of conflicting or insufficient evidence. These findings suggest that the proposed architecture provides a viable foundation for explainable and reliable decision support in modern poultry production, achieving consistent reasoning under contradictory or incomplete information where conventional RAG chatbots may produce unstable guidance.

AgriEngineering doi: 10.3390/agriengineering8030113

Authors: Hina Shafi Ali Ghulam Mir. Sajjad Hussain Talpur Rahu Sikander

It is necessary to understand the complicated interplay between diseases and medicinal plants to find new curing agents that may be used in natural sources. Nevertheless, the state of interaction between diseases and plants today is not fully developed yet, and the potentially productive plant-based treatment can hardly be identified rationally. In order to elaborate on this challenge, we will offer a heterogeneous network approach to the prediction of novel disease–plant associations by using the Random Walk with Restart (RWR) algorithm. The framework combines three significant relational networks, including (i) a disease–plant association network, which has been built using curated literature and biological databases, (ii) a disease–disease similarity net, which is constructed using shared symptoms and therapeutic profiles, and (iii) a plant–plant similarity net using phytochemical and functional similarities. These elements are integrated into a homogeneous graph that is heterogeneous in nature, and thus, information flows through related nodes. The model begins by finding RWR between known disease or plant nodes and develops the network by exploring the graph further to make estimates of the probability of association between disease and plant networks that were not previously connected. Experimental tests show that the proposed model has an excellent predictive ability, ROC-AUC of 0.9987, PR-AUC equal to 0.915, and Precision = 10 of 1.0, significantly better than the results of the base models, including Random- and Degree-based models. The bootstrap analysis supported the strength of the model as the mean ROC-AUC was 0.9987 with a standard deviation of 0.00051. The suggested structure offers an effective computational methodology to systematically explore disease–plant interactions to aid in finding novel herbal drugs to treat diseases and speed up the drug discovery process by means of inference based on networks.

AgriEngineering doi: 10.3390/agriengineering8030112

Authors: Haiyang Shen Guangyu Xue Gongpu Wang Wenhao Zheng Lianglong Hu Yanhua Zhang Baoliang Peng

Driven by agricultural labor shortages and rising quality requirements, ginger harvesting increasingly demands high-throughput, low-damage operations and a reliable supply chain. This review summarizes harvesting modes and harvester types used in ginger production, with emphasis on critical process modules: digging and lifting, soil disintegration and cleaning, vine cutting and anti-tangling, gentle conveying, and collection. We compare major technical routes in terms of field capacity, control of soil and foreign materials, damage mitigation, and reliability under continuous operation, and identify the conditions under which each route performs best. Drawing on advances in harvesting systems for other root and bulb crops, we outline transferable approaches for intelligent sensing, precision control, and system-level integration. We then propose an online monitoring and closed-loop regulation framework for strongly coupled conditions, such as heavy clay soils, plastic-mulch residues, and vine interference. Key bottlenecks include limited cross-regional adaptability, persistent trade-offs between low damage and high throughput, cost constraints on intelligent functions, and the lack of shared datasets and standardized evaluation protocols. Future progress should be anchored in integrated equipment sets and supporting operating specifications, guided by multi-source sensing-based quality indicators and interpretable control strategy libraries, to reduce harvest losses, stabilize marketable quality, improve operational efficiency, and enable scalable adoption.

AgriEngineering doi: 10.3390/agriengineering8030111

Authors: Alexsander Toniazzo de Matos Tatiane Fernandes Adriana Sathie Ozaki Hirata Ingrid Harumi de Souza Fuzikawa Alexandre Rodrigo Mendes Fernandes Adrielly Lais Alves da Silva Rodrigo Andreo Santos Ariadne Patrícia Leonardo Aylpy Renan Dutra Santos Fernando Miranda de Vargas Junior

Sheep farming length of stay in the feedlot directly influences system profitability, mainly due to the high cost of feed. Thus, the use of predictive models based on body measurements is an important tool to define the optimal slaughter point and the ideal feedlot period. Thus, the aim was to evaluate predictive models of meat cuts and tissue carcasses concerning weight at slaughter (WS), loin eye area (LEA), and subcutaneous fat thickness (SFT) obtained by ultrasound of the lumbar region of lambs. The WS and ultrasound measurements were obtained from a pre-slaughter collection of 45 lambs, divided into five groups, each weighing 15, 20, 25, 30, or 35 kg, with nine replications per group. Three regression models were evaluated: WS, LEA, and SFT (independent variables) and the cuts yield or tissue composition (dependent variable). Increasing WS resulted in greater carcass weight and commercial cuts. Above 15 kg body weight, bone weight showed little or no increase (allometric coefficient = 0.06), whereas muscle and fat tissues increased steadily, with allometric coefficients of 0.25 and 0.12, respectively. The commercial cuts showed a high and significant correlation with WS and LEA. The muscle and bone proportion of the leg had a significant (p < 0.10) correlation with SFT. For the weight of commercial cuts estimates, the inclusion of LEA and/or SFT with WS did not improve the coefficient of determination but made the predictions equivalent to the measured values. There were high determination coefficients when WS was only used to predict muscle, fat, and bone weight, but it was not efficient in predicting the muscle/fat and muscle/bone ratios and the percentage of tissues. The WS was the variable that best explained the weight and tissue content. The inclusion of LEA and/or SFT made little improvement to the predictive models.

AgriEngineering doi: 10.3390/agriengineering8030110

Authors: Nikolaos Tsigkas Vasileios Anestis Anna Vatsanidou Chrysanthos Maraveas

The current research undertook a comprehensive examination of global research related to the use of measurement, reporting, and verification (MRV) techniques for quantifying and tracking greenhouse gas (GHG) emissions from agriculture and livestock farming. Data were collected using a bibliometric analysis of 5340 studies published in the period (1990–2025) and a systematic literature review of 100 studies published in the period (2020–2025). The insights from the findings showed that four MRV techniques were broadly adopted across different regions: (1) inventory techniques (IPCC Tiers, national systems), (2) accounting at the project/product level (LCA, carbon footprint protocols), (3) MRV based on measurement and models (chambers, remote sensing, farm models, AI/ML), and (4) frameworks for governance and standardization (UNFCCC, Paris ETF, PAS 2050, etc.). The findings further revealed the impact of the MRV techniques on agriculture and livestock farming, showing that they facilitated the uptake of low-carbon practices. In agriculture, the MRV techniques showed that lower emissions emerged from mixed cropping, while in livestock farming, the emissions varied based on the feeding stage and type of diet used. However, various challenges arose in the adoption of MRV techniques where there was limited data related to GHG emissions, thereby reducing generalizability. In future work, there is a need for scholars to consider integrating the different MRV techniques to develop an understanding of the problem area.

AgriEngineering doi: 10.3390/agriengineering8030109

Authors: Oscar Andrés Martínez Kevin David Ortega Quiñones German Andrés Holguin-Londoño

Crop monitoring over large land extensions represents a central challenge in precision agriculture, especially in polyculture contexts where species with different nutritional needs are combined. This study presents a methodology to manage and analyze large volumes of multispectral images captured by unmanned aerial vehicles (UAVs) in order to identify and monitor crops at the plant level. The images are efficiently stored and retrieved using a Hilbert Curve, which reduces the complexity of the search process from O(n2) to O(log(n)) where n represents the number of indexed data points). The system connects to a distributed Structured Query Language (SQL) database, allowing for fast image retrieval based on GPS coordinates and other metadata. Additionally, the Normalized Difference Vegetation Index (NDVI) is calculated using reflectance data from the red and near-infrared channels, adjusted by semantic segmentation masks generated with a U-Net model, which allows for species-specific evaluations. The methodology was evaluated on a 20,000 m2 polyculture farm with coffee, avocado, and plantain crops, using a dataset of 270 aerial images partitioned into 70% for training and 30% for validation. The results show improvements in retrieval speed and precision with the Hilbert Space-Filling Curve (HSFC) approach, and an accuracy of 82.3% and an the Mean Intersection over Union (MIoU) of 68.4% in species detection with the U-Net model. Overall, this integrated framework demonstrates a scalable potential for precision agriculture in complex polyculture systems, facilitating efficient data management and targeted crop interventions.

AgriEngineering doi: 10.3390/agriengineering8030108

Authors: Laís Helena Sousa Vieira Francisca Gleiciane da Silva Laís Gomes Fregolente Ícaro Vasconcelos do Nascimento Rafaela Batista Magalhães Francisco Luan Almeida Barbosa Gilvanete da Silva Henrique Maria Vitória Ricarte Gonçalves Bruno Eduardo Lopes Sousa Eduardo Custódio Vilas Boas Amauri Jardim de Paula Helon Hébano de Freitas Sousa Arthur Prudêncio de Araujo Pereira Jaedson Cláudio Anunciato Mota Mirian Cristina Gomes Costa Odair Pastor Ferreira

The growing demand for sustainable soil management strategies has intensified interest in hydrochar (HC), a waste-derived amendment produced via hydrothermal carbonization (HTC). This review synthesizes recent advances in HC production, characterization, and agri-environmental applications within a waste-to-resource framework. It covers studies conducted mainly over the last decade, encompassing a wide range of feedstocks, including agricultural residues, sewage sludge, animal manures, and food waste. HTC is typically performed at 130–280 °C under autogenous pressure (2–15 MPa), generating HCs with low intrinsic surface area (<50 m2g−1) and oxygen-containing functional groups that govern nutrient dynamics and soil interactions. Reported application rates vary broadly between 10 and 60 t ha−1, with most experiments conducted under greenhouse conditions. Positive effects on soil pH, cation exchange capacity, water retention, and phosphorus availability are frequently observed. However, plant responses vary according to the type of stimulation promoted by HC, as well as its processing conditions, application rates, and the soil characteristics in which it is applied. Advanced molecular-level analyses (e.g., FT-ICR-MS, GC-MS, and 13C-NMR) have provided mechanistic insights into carbon stability, nutrient release, and interaction with soil organic matter. Reusing HTC process water offers an additional pathway for nutrient recovery, although concerns about phytotoxic compounds remain. Despite promising short-term results, long-term field evaluations and standardized assessment protocols are still limited. This review integrates structural, functional and agri-environmental perspectives to identify critical knowledge gaps and guide the optimized and context specific use of hydrochar in sustainable agricultural systems. At the same time, it emphasizes its role in advancing carbon sequestration and in operationalizing resource-circular strategies, thereby underscoring its broader practical and strategic relevance.

AgriEngineering doi: 10.3390/agriengineering8030107

Authors: Qiantao Sun Huan Qin Jibang Hu Huaigang Guo Dongwei Wang Wenxi Sun

To optimize the structural parameters of a peanut digging shovel and enhance its operational performance, the forces exerted on the digging shovel were examined through a graphical mechanics approach. This analysis identified the primary structural and operational parameters of the shovel’s design. A numerical simulation model for the working resistance of the shovel was established adopting EDEM (2018) discrete element analysis software and subsequently validated through comparative analysis with field experiment results. Employing the Box–Behnken response surface method, quadratic regression models were constructed with digging resistance and soil non-breakage ratio as the response variables, while forward speed, soil entry angle, and blade tilt angle were taken as the influencing factors. Optimization analysis of these parameters was conducted. The optimization results indicate that with a forward speed of 0.8 m/s, a soil entry angle of 20°, and a blade tilt angle of 40°, the working resistance of the shovel is 1667 N, and the soil non-breakage ratio is 20.56%. The error between the field test results and the predictions from the optimized model was less than 2%, illustrating the feasibility of the model and the optimization outcomes. This study offers a technical reference for future simulation-based optimization of peanut digging shovels.

AgriEngineering doi: 10.3390/agriengineering8030106

Authors: Xiao Yang Ji Shi Kailin Yang Xiaoqing Lian Shufeng Zhang Hongbiao Wang Zheng Li

Mulch-film covering technology has been widely adopted in cotton production in arid regions; however, the associated problem of residual-film pollution has become increasingly prominent, creating an urgent demand for efficient and accurate monitoring approaches. Owing to the small target scale, irregular morphology, blurred boundaries, and complex soil backgrounds of residual-film fragments, residual-film detection based on close-range UAV imagery remains a challenging task. To address these issues, this study proposes an improved algorithm, termed YOLO-EGASF, for residual-film detection in arid-region cotton fields, built upon the lightweight YOLOv11n framework. To enhance the detection of small targets with weak boundary characteristics, the baseline model is improved from three aspects. First, a boundary-enhanced multi-branch small-target extraction module (EMSE) is designed to reinforce shallow-layer details and gradient information through multi-scale convolution and explicit edge enhancement. Second, a GLoCA attention module that integrates global coordinate information with local geometric features is constructed to improve the discriminative capability of the model for residual-film targets under complex background conditions. Third, an ASF-layer multi-scale feature fusion structure is introduced, together with an additional small-target detection layer, to strengthen the participation of high-resolution features in cross-scale fusion and prediction. Experimental results on a self-constructed UAV-based residual-film dataset from cotton fields demonstrate that YOLO-EGASF outperforms several mainstream detection models in terms of Precision, Recall, mAP@0.5, and mAP@0.5:0.95, achieving mAP@0.5 and mAP@0.5:0.95 values of 71.9% and 26.8%, respectively. These results indicate a significant improvement in detection accuracy and robustness, confirming that the proposed method can effectively meet the practical requirements of fine-grained residual-film monitoring in arid-region cotton fields.

AgriEngineering doi: 10.3390/agriengineering8030105

Authors: Maria T. R. Silva Fábio L. Santos Rafaella V. Pereira Francisco Scinocca

The mechanization of the agricultural sector exposes operators to vibrations generated by tractors, terrain inclination, and attached implements. Prolonged exposure to such vibrations can lead to health problems, including visual disturbances, fatigue, spinal injuries, and low back pain. In this context, the present study aimed to assess the severity of mechanical vibrations in an agricultural tractor with four-wheel drive, both as a standalone unit and as part of a mechanized assembly comprising the same tractor coupled to a fertilizer seeder during sowing operations. Vibrations were monitored at four data collection points: the front and rear axles, the cab floor, and the operator’s seat. Root mean square (RMS) acceleration values were compared with the limits established by ISO 2631-1, and the comfort levels at the operator’s seat were classified as “uncomfortable” and “very uncomfortable.” Vibration transmissibility between the rear axle and the cab floor (T2) was found to exceed 1, indicating amplification of vibrations. Overall, the operator’s seat attenuated the vibration severity transmitted to the operator. Forward speed significantly influenced vibration severity, with higher speeds associated with increased RMS accelerations. Slope also affected vibration levels, with slope D2 (the sloped area) presenting higher mean RMS acceleration values. Notably, the tractor operating with the seeder fertilizer exhibited attenuated vibration levels compared to the tractor alone.

AgriEngineering doi: 10.3390/agriengineering8030104

Authors: Ioannis-Vasileios Kyrtopoulos Dimitrios Loukatos Emmanouil Zoulias Chrysanthos Maraveas Konstantinos G. Arvanitis

The increasing demand for agricultural products, intensified by natural resource degradation and the lack of human labor in the agri-food sector, favors the adoption of advanced automated technologies in the entire farm-to-fork chain. Despite skepticism, 3D (three-dimensional) printing is amongst the methods that have drawn increasing attention and encourage expectations for tackling the aforementioned challenges. In this context, the current work has a multiperspective character. Firstly, it sheds light on the recent progress in the 3D printing fabrication area and focuses on laboratory-implemented parts improving the efficiency of typical agricultural processes. These cost-effective solutions vary from covers for damaged electric water pumps and joints for greenhouse structures to adjustable ventilation grilles, automatic irrigation valves and specialized fruit-harvesting grippers. Secondly, it reports on lessons learned, highlighting potential strengths/weaknesses during the fabrication process, assisted by complementary feedback collected via questionnaires from agricultural engineering students, their professors, and farmers. Experiences gained justify the optimism about the capacity of 3D printing to foster agriculture, but there are still concerns about the easiness of the 3D printing process and the ability of the 3D-printed parts to withstand harsh agricultural field conditions. Finally, it indicates future directions for the incorporation of 3D printing in agriculture toward increased sustainability pathways.

AgriEngineering doi: 10.3390/agriengineering8030103

Authors: Nelly Kichamu Putri Kusuma Astuti Szilvia Kusza

Goats are important livestock species in most rural households and were amongst the first species to be domesticated. Despite this, their production is based on extensive systems, exposing them to numerous challenges affecting their productivity. This review examines the applications of precision livestock farming (PLF) and AI-driven technologies in goat management, focusing on their impacts on productivity, welfare, genetic potential, health monitoring, feeding efficiency and sustainability outcomes and identifying challenges for their adoption in smallholder and extensive systems. Unlike previous reviews that focus mainly on cattle raised under intensive systems, this review synthesizes their use in goat production and highlights technological, socio-economic and infrastructural constraints. A conventional literature review approach is used, with studies retrieved from major databases using relevant keywords. The selected studies are evaluated to assess technological applications, benefits and adoption challenges, followed by a SWOT analysis. Engineering aspects of precision livestock farming—including sensors, data connectivity, system integration, automation and scalability—are also discussed. Ideally, these technologies operate as integrated decision-support systems that jointly improve productivity, animal welfare and sustainability, rather than performing isolated tasks. However, many PLF solutions remain at low technology-readiness levels and are constrained by infrastructure gaps, sensor reliability and compatibility issues, which collectively limit adoption in smallholder systems. Future research should focus on the development of cost-effective, reliable PLF systems for smallholder producers, while policy and capacity-building initiatives are needed to enhance infrastructure, training and technology adoption for scalable implementation.

AgriEngineering doi: 10.3390/agriengineering8030102

Authors: Henrique Fonseca Elias de Oliveira José Henrique Nunes Flores Lessandro Coll Faria Samuel Beskow Giuliani do Prado Gustavo Borges Lima Jhon Lennon Bezerra da Silva Marcos Vinícius da Silva Alberto Colombo

Wind is a primary driver of nonuniform water application in traveling gun sprinklers, yet design guidance often treats wind only as speed. This study quantifies how diurnal wind regimes (day vs. night) and wind incidence relative to the towpath (φ) affect application-rate patterns and the Christiansen uniformity coefficient (UC) as a function of towpath spacing expressed as a fraction of wetted diameter (WD). Class-specific sprinkler patterns were generated with the Simulation Model for Sprinkler Irrigation (SIA) and combined with local daytime and nighttime wind-frequency data to build composite application-rate fields; these drove traveler simulations that computed cross-track depth, lateral overlap across spacings, and UC for representative wind speeds (0–6 m s−1) and φ (0°, 45°, 90°). Nighttime operation yielded higher UC, with a day–night crossover near ~50% WD and an average UC gain of ~9.5 percentage points; typical gains were +6 to +9 points between 55% and 90% WD. Wind incidence was as influential as speed: at 65.6% WD, increasing wind from 0 to 6 m s−1 reduced UC from 84.4% to 28.6% for φ = 0°, to 52.0% for 45°, and to 76.1% for 90°. Findings support nighttime scheduling, towpaths avoiding wind-parallel operation, and tighter spacings under windy conditions.

AgriEngineering doi: 10.3390/agriengineering8030101

Authors: Cíntia Cristina Soares Jamile Raquel Regazzo Thiago Lima da Silva Marcos Silva Tavares Fernanda de Fátima da Silva Devechio Ronilson Martins Silva Adriano Rogério Bruno Tech Murilo Mesquita Baesso

The automatic detection of foliar nutritional deficiencies through computer vision represents a promising alternative within precision agriculture practices, reducing dependence on laboratory analyses and the subjectivity associated with visual inspection. This systematic review maps and compares the application of machine learning (ML) and deep learning (DL) techniques to nutritional diagnosis across different crops, highlighting methodological trends, barriers to model adoption under field conditions, and existing research gaps. Following the PRISMA guidelines (PRISMA-P and PRISMA-2020), searches were conducted in the Scopus, IEEE Xplore, and Web of Science databases, using a defined time frame and explicit inclusion and exclusion criteria, resulting in 200 articles included (2012–2026; last search on 2 February 2026). The results indicate a predominance of DL-based approaches and RGB imagery, with applications concentrated in crops such as rice and in macronutrients, mainly nitrogen (N), phosphorus (P), and potassium (K), and report a marked increase in publications from 2020 onward. Although many studies report high performance, the evidence is largely derived from controlled environments and proprietary datasets, which limit model comparability, reproducibility, and generalization to real-world scenarios. Accordingly, the main research gaps include limited validation under field conditions, identified as the primary practical barrier; the underrepresentation of micronutrients and multiple-deficiency diagnosis; and the need for lightweight architectures suitable for deployment in mobile and edge-computing applications. It is concluded that ML and DL techniques offer promising alternatives for automated nutritional diagnosis; however, advances in data standardization, open-access datasets, and validation under real field conditions are essential for consolidating these technologies in practical applications.