AgriEngineering

Mechanical damage like bruises produced during postharvest handling can lower market value, affect nutritional value, and pose food safety risks. The study evaluated bruises on apples using image processing. This research focuses on using computer vision for apple fruit damage detection. The fruits were subjected to three levels of impact using three ball weights (66, 98, and 110 g) dropped from 50 cm height and stored at 22 °C. The overall impact energies generated were 0.323 J (low), 0.480 J (medium), and 0.539 J (high). The bruise area and susceptibility of the damage, surface area of the fruit, and color were measured manually (colorimeter) and by image processing. The study found that the bruise area was significantly affected by impact force, where 110 g (0.539 J) damaged apples showed a bruise area of 4.24 cm² after 21 days of storage at 22 °C. The images showed a significant change in the RGB values (Red, Green, Blue) over 21 days of storage when impacted at 0.539 J. The study showed that the greater the impact energy effect, the higher the weight loss under constant conditions of storage. After 21 days of storage, the 110 g mechanically damaged apples recorded the highest percentage of weight loss (6.362%). The study found a significant decrease in the surface area of 110 g bruised apples, with a smaller decrease in surface area for 66 g bruised fruit. The use of computer vision to detect bruise damage and other quality attributes of Granny Smith apples can be highly recommended to detect their losses. Full article

Nitrogen fertilization is decisive for maize productivity, fertilizer use efficiency, and sustainability, which calls for fast and nondestructive nutritional diagnosis. This study evaluated the classification of maize plant nutritional status from red, green, and blue (RGB) leaf images using texture attributes. A greenhouse experiment was conducted under a completely randomized factorial design with four nitrogen doses, one maize hybrid Pioneer 30F35, and four replicates, at two sampling times corresponding to distinct phenological stages, totaling thirty-two experimental units. Images were processed with the gray-level cooccurrence matrix computed at three distances 1, 3, and 5 pixels and four orientations 0°, 45°, 90°, and 135°, yielding eight texture descriptors that served as inputs to five supervised classifiers: an artificial neural network, a support vector machine, k nearest neighbors, a decision tree, and Naive Bayes. The results indicated that texture descriptors discriminated nitrogen doses with good performance and moderate computational cost, and that homogeneity, dissimilarity, and contrast were the most informative attributes. The artificial neural network showed the most stable performance at both stages, followed by the support vector machine and k nearest neighbors, whereas the decision tree and Naive Bayes were less suitable. Confusion matrices and receiver operating characteristic curves indicated greater separability for omission and excess classes, with D1 standing out, and the patterns were consistent with the chemical analysis. Future work should include field validation, multiple seasons and genotypes, integration with spectral indices and multisensor data, application of model explainability techniques, and assessment of latency and scalability in operational scenarios. Full article

Coriander is a significant crop, playing an essential role in daily life for various purposes, including flavouring curries and medicinal uses, among others. Despite its importance, coriander is still harvested manually. To address this, developed a self-propelled battery-operated coriander harvester, designed with ergonomics, environmental sustainability and affordability for small and marginal farmers in mind. The harvester is equipped with a main frame, a lead-acid battery, a BLDC motor, a reciprocating cutter bar, a PU conveyor belt, a collection bag, a handle, and transport wheels. The harvester was tested on the coriander crop, and the results were analyzed using Design Expert software to optimize various operational parameters. The harvester’s performance was evaluated at three forward speeds: 1.5 km/h, 2 km/h, and 2.5 km/h, resulting in covered areas of 0.114 ha, 0.164 ha, and 0.22 ha, with field efficiency values of 76%, 82%, and 88%, respectively. Optimal harvesting conditions were identified by design expert software at a forward speed of 1.64 km/h, with a conveyor driving pulley at level 3 (50.8 mm) and a cutting height at level 2 (75 mm). Under these conditions, the harvester achieved a harvesting efficiency of 97.24% and a cutting efficiency of 98.2%, with minimal conveying loss of 0.96%. The theoretical field capacity was 0.16 ha/h, the actual field capacity was 0.131 ha/h, and the overall field efficiency was 81.8%. Full article

Digital twins, defined as virtual counterparts of physical systems that evolve with sensor data have potential applications in controlled-environment agriculture. This study previews the integration of adaptive Microclimate Monitoring within a Unity-based digital twin of a strawberry greenhouse to support dynamic sensor selection and reallocation. Using data collected from 56 distributed temperature–relative humidity sensors, a Thompson Sampling algorithm was deployed to assign monthly importance rankings and identify season-specific subsets of sensors. To evaluate how well these subsets represented the whole sensor network, we used the Z-index, which measures distributional consistency. Across all observed months, Z-index values remained close to zero, with values of 0.037 in February, 0.012 in April, −0.002 in June, and 0.025 in October for relative humidity. These results indicate that the digital twin framework sustains the overall climate trend while reducing sensing redundancy, pointing to its potential role in future climate monitoring strategies within greenhouse systems. Full article

The efficient degradation of lignin from agricultural by-products is a critical step in the development of sustainable bioprocessing technologies for waste valorisation. Enzymatic degradation of kraft lignin performed with lignin peroxidase (LiP), manganese peroxidase (MnP), and laccase (Lac) was investigated. A response surface methodology (RSM) based on a Box–Behnken Design (BBD) was employed in order to optimise key process parameters including enzyme concentration, lignin concentration, pH, incubation temperature, and activator concentration. The surface plots were used to determine the best conditions for each enzyme in order to better degrade kraft lignin. Therefore, LiP needed a stronger acidic environment and moderate temperature, MnP needed an almost neutral pH and moderate temperature, and Lac needed a neutral pH and higher temperature. This work contributes to the development of smart agricultural waste management practices by combining enzymatic treatments with statistical modelling for process optimisation. This study provides a framework for lignin degradation that can be used as a starting point for diverse lignocellulosic by-product fragmentation, thus supporting a circular bioeconomy initiative in accordance with today’s trends. The optimised enzymatic parameters could help enhance efficiency, enable process standardisation across feedstocks, and support economically and environmentally sustainable industrial-scale lignin valorisation in integrated biorefineries. Full article

Weed detection is critical for precision agriculture, enabling targeted herbicide application to reduce costs and enhance crop health. This study utilized UAV-acquired RGB imagery from cotton fields to develop and evaluate deep learning models for weed detection. As sustainable resource management gains importance in rainfed agricultural systems, precise weed identification is essential to optimize yields and minimize herbicide use. However, distinguishing weeds from crops in complex field environments remains challenging due to their visual similarity. This research employed YOLOv7, YOLOv7-w6, and YOLOv7-x models to detect and classify weeds in cotton fields, using a dataset of 9249 images collected under real field conditions. To improve model performance, we enhanced the annotation process using LabelImg and Roboflow, ensuring accurate separation of weeds and cotton plants. Additionally, we fine-tuned key hyperparameters, including batch size, epochs, and input resolution, to optimize detection performance. YOLOv7, achieving the highest estimated accuracy at 83%, demonstrated superior weed detection sensitivity, particularly in cluttered field conditions, while YOLOv7-x with accuracy at 77% offered balanced performance across both cotton and weed classes. YOLOv7-w6 with accuracy at 63% faced difficulties in distinguishing features in shaded or cluttered soil regions. These findings highlight the potential of UAV-based deep learning approaches to support site-specific weed management in cotton fields, providing an efficient, environmentally friendly approach to weed management. Full article

Accurate and consistent measurements of geometric features such as fruit length and width are essential for the quality assessment of Namdokmai Sithong mangoes. Traditional manual methods are labor-intensive and prone to inconsistency. This study presented an automated system for geometric feature extraction of Namdokmai Sithong mangoes using a YOLOv8-based object detection model. The framework automated the process of measuring key morphological traits, specifically fruit length and width, to improve accuracy and consistency in quality assessment. The model identified an anatomically meaningful reference point for guiding axis-based measurements by detecting the mango and its peduncle. HSV-based image segmentation combined with morphological operations and edge detection effectively calculated the major (length) and minor (top and bottom width) axes of the fruit. Evaluation on 30 test images showed that the proposed method achieved error rates below 5% in over 90% of samples, with average deviations for fruit length typically under 1.5%. The system was implemented as a standalone Python (version 3.12.8) application and demonstrated high potential for use in real-time, automated fruit grading scenarios. Full article

The increasing generation of agricultural waste poses both environmental and economic challenges, particularly in rural areas with limited infrastructure. Anaerobic digestion has emerged as a sustainable alternative, enabling the valorization of waste and the production of biogas and biofertilizer. This study evaluated the economic and environmental gains of mono- and co-digestion of equine manure and vegetable waste using biodigesters of different capacities across four simulated projects—Project 1 (15 m² biodigester with monodigestion), Project 2 (15 m² biodigester with co-digestion), Project 3 (20 m² biodigester with monodigestion), and Project 4 (20 m² biodigester with co-digestion). Economic feasibility was assessed through indicators such as Net Present Value (NPV), Internal Rate of Return (IRR), Modified IRR (MIRR), Profitability Index (PI), Benefit-Cost Ratio (B/C), Discounted Payback Period, sensitivity analysis, and Monte Carlo simulation, adopting a Minimum Attractiveness Rate (MAR) of 6.43% per year. Environmental benefits were estimated based on the annual reduction of CO₂ equivalent emissions. The results showed that all projects were economically viable and had the potential to mitigate up to 36 tons of CO₂eq per year. Additionally, an eco-efficiency indicator (NPV per CO₂eq avoided) was calculated to enable an integrated assessment of economic performance and environmental impact. Projects using 20 m³ biodigesters achieved the best results, with Project 3 being the most eco-efficient (USD256.05/tCO₂eq), while Project 4 yielded the highest absolute return in all economic analysis tools: NPV (USD 9063.81), IRR (25.10%), MIRR (10.95%), PI (USD 1.65), B/C (USD 1.65) and DPP (4.56 years). The integrated analysis underscores the significance of co-digestion and economies of scale in encouraging the adoption of this technology by small rural producers. Full article

This study evaluates the positional accuracy of Global Navigation Satellite Systems (GNSS) and Unmanned Aerial vehicle (UAV)-based LiDAR systems in terrain modeling, using a total station as a reference. The research was conducted over 17 Ground Control Points (GCPs), with measurements obtained using a CHCNAV i50 GNSS receiver and a DJI Zenmuse L1 Light Detection and Ranging (LiDAR) sensor mounted on a UAV. Accuracy was assessed for horizontal (X, Y) and vertical (Z) components by comparing the results against total station data. Errors were quantified using statistical metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and RMS at 1σ. GNSS exhibited superior horizontal accuracy with an RMS 1σ of 1.1 cm, while LiDAR achieved 1.7 cm. In contrast, GNSS outperformed LiDAR in vertical precision, achieving a 1σ RMS of 6.4 cm compared to 6.6 cm for LiDAR. These findings align with manufacturer specifications and international standards such as those of the American Society for Photogrammetry and Remote Sensing (ASPRS). The results highlight that GNSS is preferable for applications requiring high horizontal precision, while LiDAR is better suited for vertical modeling and terrain analysis. The combination of both systems may offer enhanced results for comprehensive geospatial surveys. Overall, both technologies demonstrated sub-decimetric accuracy suitable for precision agriculture, civil engineering, and environmental monitoring. Full article

The mechanisation of sesame (Sesamum indicum L.) planting remains a significant challenge due to the crop’s small, fragile seeds and non-uniform shape, which hinder the effectiveness of standard seeding systems. Crop emergence and production are adversely affected by poor singulation and uneven seed distribution, which are frequently caused by conventional and general-purpose planting equipment. For sesame, consistency in seed distribution and emergence is very important, necessitating careful consideration of agronomic conditions as well as seed properties. This study was conducted as a systematic review following the PRISMA 2020 guidelines to critically evaluate the existing literature on advanced planting methods that prioritise precision, efficiency, and seed protection. A comprehensive search was conducted across Scopus, Web of Science, and Google Scholar for peer-reviewed studies published from 2000 to 2025. Studies focused on the agronomic parameters of sesame, planting technologies, and/or simulation integration, such as Discrete Element Modelling (DEM), were included in this review, and studies unrelated to sesame planting or not available in full text were excluded. The findings from these studies were analysed to examine the interaction between seed metering mechanisms and seed morphology, specifically seed thickness and shape variability. Agronomic parameters such as optimal seed spacing, sowing depth, and population density are analysed to guide the development of effective planting systems. The review also evaluates limitations in existing mechanised approaches while highlighting innovations in precision planting technology. These include optimised seed plate designs, vacuum-assisted metering systems, and simulation tools such as DEM for performance prediction and system refinement. A total of 22 studies were included and analysed using systematic narrative synthesis, grouped into agronomical, technological, and simulation-based themes. The studies were screened for methodological clarity, and reference list screening was performed to reduce reporting bias. In conclusion, the findings of this research support the development of crop-specific planting strategies tailored to meet the unique requirements of sesame production. Full article

The definition of management zones is an essential strategy for optimizing agricultural productivity, enabling the efficient use of inputs and the minimization of environmental impacts. This study aims to identify and classify management zones in a cultivated area, considering spatial variations in soil fertility and carbon content. The methodology employed includes the analysis of spatial data through geotechnologies, combined with fuzzy logic for categorizing areas into management classes. The results indicate that lower-quality regions present distinct edaphic characteristics, being mostly composed of dystrophic Red-Yellow Argisol, which negatively affects productivity due to lower water retention capacity and poor fertility. In addition, a correlation between soil carbon content and fertility was identified, showing that areas with lower carbon stocks tend to be less productive. The application of these techniques allowed for a more precise approach to agricultural management, promoting sustainable practices that enhance productive efficiency and reduce environmental degradation. Full article

Estimated population growth and increased demand for food production bring with them the evident need for more efficient and sustainable production systems. Because of this, computer vision plays a fundamental role in the development and application of solutions that help producers with the issues that limit livestock production in Brazil and the world. In addition to being stressful for the producer and the animal, the conventional pig weighing system causes productive losses and can compromise meat quality, being considered a practice that does not value animal welfare. The objective was to develop a computational procedure to predict the live weight of pigs in the growth and finishing phases, through the volume of the animals extracted through the processing of 3D images, as well as to analyze the real and estimated biometric measurements to define the relationships of these with live weight and volume obtained. The study was conducted at Roçadinho farm, in the municipality of Capoeiras, located in the Agreste region of the state of Pernambuco, Brazil. The variables weight and 3D images were obtained using a Kinect^®—V2 camera and biometric measurements of 20 animals in the growth phase and 24 animals in the finishing phase, males and females, from the crossing of Pietrain and Large White, totaling 44 animals. To analyze the images, a program developed in Python (PyCharm Community Edition 2020.1.4) was used, to relate the variables, principal component analyses and regression analyzes were performed. The coefficient of linear determination between weight and volume was 73.3, 74.1, and 97.3% for pigs in the growing, finishing, and global phases, showing that this relationship is positive and satisfactorily expressed the weight of the animals. The relationship between the real and estimated biometric variables had a more expressive coefficient of determination in the global phase, having presented values between 77 and 94%. Full article

Given the demand for sustainable and cost-effective manure management in livestock systems, this study evaluated the economic feasibility of cattle manure treatment via composting and anaerobic digestion (AD) under different configurations. Five scenarios were compared: composting without solid–liquid separation, AD without separation at 20- and 30-day hydraulic retention times (HRTs), and combined systems with separation, composting the solid fraction and digesting the liquid. The analysis was based on a 200-cow herd and experimental data, with 15-year projected cash flows. Economic indicators included net present value (NPV), internal rate of return (IRR), discounted payback period (DPP), benefit–cost ratio (B/C), modified internal rate of return (MIRR), uniform annual equivalent (UAE), and profitability index (PI), supported by sensitivity analysis and Monte Carlo simulation. All scenarios were viable and posed low risk. Energy and fertilizer value were key drivers. The scenario 30-day HRT without separation had the best financial performance (NPV = 53,407.15 USD; IRR = 15.54%; DPP = 7.33 years; B/C = 1.57; MIRR = 9.28%; UAE = 5654.48 USD; PI = 1.66) and is recommended for capitalized farms seeking higher returns. Composting had lower returns (NPV = 9832.06 USD) but required the lowest investment, remaining a cost-effective alternative for smallholders. Full article

A rice grain’s proximate composition determines its nutritional potential. Macronutrient quantification is essential to identify superior genotypes and direct breeding efforts to reach more people who are vulnerable. Conventional methods to determine proximate composition are highly accurate; however, they remain time-consuming, costly, and destructive. Near-infrared (NIR) spectroscopy enables proximate composition analysis in a non-destructive, rapid, inexpensive, and practical manner, providing results similar to well-established conventional methods. This study aimed to evaluate the feasibility of NIRs-based selection to identify more nutritious rice genotypes. A collection of 155 rice genotypes grown in Southern Brazil was used. After harvest, grains were hulled, polished, and milled. NIRs was used to determine moisture, starch, protein, fat, ash, and fiber contents in rice flour. It was possible to differentiate genotypes with higher and lower levels of the investigated components. Similar and distinct values were observed in comparison to other studies, indicating the accuracy of NIRs and the effect of genotype and environment, respectively. Starch is correlated negatively with protein and fat, preventing the identification of genotypes with high levels of these three components. PCA enabled the separation of the genotypes but highlighted the complexity of sample distribution. NIRs is an effective and accurate method to determine the proximate composition of rice, enabling the selection of more nutritious genotypes. Full article

Compacted and degraded soils pose increasing challenges to agricultural practices, necessitating innovative approaches to soil tillage. This paper evaluates the performance of a vibratory-enhanced moldboard plowing system, designed to improve energy efficiency and tillage quality under compacted and moisture-deficient conditions, typical of low-moisture soils. Field experiments were conducted across four distinct Romanian regions with varying soil types and climatic conditions, all characterized by significant compaction and limited soil moisture. The vibratory system, mounted directly on each plow body, employed sinusoidal oscillations generated by a DC moto-vibrator, to reduce soil adhesion and traction force requirements, thereby lowering fuel consumption. Key parameters including fuel consumption, working speed, soil fragmentation, weed incorporation, and traction force were measured and compared with the conventional plowing method. The results showed enhanced soil fragmentation and more effective residue incorporation, along with notable reductions in traction effort and fuel use at optimal oscillation settings. These findings highlight the potential of vibratory tillage to be used as a soil preparation method for compaction-prone areas, improving the soil structure while increasing operational energy efficiency. Full article

The rapid and accurate identification of pathogenic spores is essential for the early diagnosis of diseases in modern agriculture. Gray mold disease, caused by Botrytis cinerea, is a significant threat to several crops and is traditionally controlled using fungicides or, alternatively, by UV-C radiation. Classically, the determination of conidial germination percentage, a key indicator for assessing pathogen viability, has been a manual, time-consuming, and error-prone process. This study proposes an approach based on deep learning, using one-stage detectors to automate the detection and counting of germinated and non-germinated conidia in microscopy images. We trained and assessed the performance of three models under several metrics: YOLOv8, YOLOv11, and RetinaNET. The results show that these three architectures provide an efficient and accurate solution for the recognition of gray mold conidia viability. Selecting the best model, we performed the task of detecting and counting conidia for determining the germination percentage on samples treated with different UV-C radiation dosages. The results show that these deep-learning models achieved counting accuracies that closely matched those obtained with conventional manual methods, yet they delivered results far more rapidly. Because they operate continuously without fatigue or operator bias, these models begin to open possibilities, after widening field tests and datasets, for efficient and fully automated monitoring pipelines for disease management in the agro-industry. Full article

Fodder maize constitutes a key economic crop in Thailand, particularly in the northeastern region, where it significantly contributes to livestock feed production and local economic development. Nevertheless, the planning and management of cultivation areas remain a major challenge, especially in urban and peri-urban agricultural zones, due to the limited availability of spatial data and suitable analytical frameworks. These difficulties are exacerbated in urban settings, where the complexity of land use patterns and high spectral similarity among land cover types hinder accurate classification. The Google Earth Engine (GEE) platform provides an efficient and scalable solution for geospatial data processing, enabling rapid land use classification and spatiotemporal analysis. This study aims to enhance the classification accuracy of fodder maize cultivation areas in Mueang District, Nakhon Ratchasima Province, Thailand—an area characterized by a heterogeneous mix of urban development and agricultural land use. The research integrates GEE with four machine learning algorithms: Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes (NB), and Classification and Regression Trees (CART). Eleven datasets were developed using Sentinel-2 imagery and a combination of biophysical variables, including elevation, slope, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI), to classify land use into six categories: fodder maize cultivation, urban and built-up areas, forest, water bodies, paddy fields, and other field crops. Among the 44 classification scenarios evaluated, the highest performance was achieved using Dataset 11—which integrated all spectral and biophysical variables—with the SVM classifier. This model attained an overall accuracy of 97.41% and a Kappa coefficient of 96.97%. Specifically, fodder maize was classified with 100% accuracy in both Producer’s and User’s metrics, as well as a Conditional Kappa of 100%. These findings demonstrate the effectiveness of integrating GEE with machine learning techniques for precise agricultural land classification. This approach also facilitates timely monitoring of land use changes and supports sustainable land management through informed planning, optimized resource allocation, and mitigation of land degradation in urban and peri-urban agricultural landscapes. Full article

This study addresses the agricultural requirement for flexible adjustment of planting spacing in seed breeding corn, designing an active rotating in-film hole-forming mechanism driven by an independent motor. The mechanism allows flexible regulation of planting spacing by adjusting the motor speed. The study first optimized the structure of the hole-forming device, selecting a rhombic duckbill as its core component and analyzing its motion trajectory and hole-forming shape. Single-factor experiments were conducted to determine the structural parameter ranges affecting film hole length. Using discrete element and multibody dynamics co-simulation, experiments were carried out with duckbill number, duckbill bottom width, and duckbill bottom height as experimental factors, and film hole length as the response variable, employing a three-factor, three-level orthogonal experimental method. Simulation results indicated that the factors influencing film hole length, in descending order of impact, were duckbill number, duckbill bottom height, and duckbill bottom width. The optimized best structural parameters were: 9 duckbills, bottom height of 351 mm, and bottom width of 22 mm, ensuring film hole length control within the range of 25–40 mm, meeting planting requirements, preventing weed growth, and ensuring a seed growth environment. Furrow testing validated the adaptability and planting performance of the mechanism under different spacing conditions, providing a theoretical basis and practical reference for the promotion of small-scale breeding and the sowing technology on the film for field seed production. Full article

Soil treatment is one of the most energy-intensive agricultural processes. While power take-off (PTO)-powered rotary tillage tools are widely used due to their operational advantages, their energy efficiency requires enhancement. A new PTO-powered rotary tillage tool was designed, with cutting blades inclined at angle β to prevent soil mass accumulation due to soil sliding along the blades, thereby enhancing energy efficiency and tillage quality. A kinematic model was developed to analyze the tool’s motion trajectories. Theoretical analysis substantiated the optimal inclination angle β = 38–42° and elliptical-profile edge configuration of the cutting blades. During field experiments for performance evaluation, the angle of attack was in the range 20° < α < 40°, and the kinematic coefficient varied in the range 1.0 < η < 1.21 in 0.07 increments. Results demonstrated that draught force and torque reduced by 1.3–1.5 and 1.1–1.4 times, respectively, with an increasing kinematic coefficient. Minimal specific total power requirements of 4.5–4.7 kW/m were obtained at the optimal kinematic coefficient, η = 1.14–1.21, and angle of attack, α = 40°. Compared to base ring tillage discs, the new design reduces total power requirements by 14–16%. Furthermore, it provides required tillage quality: soil pulverization ≥ 80%, weed cutting ≥ 97%, crop residue retention ≥ 60%, and roughness of the field soil surface ≤ 3 cm. Full article