AgriEngineering, Volume 6, Issue 3 (September 2024) – 57 articles

There is a growing need for resilient and renewable materials to aid society in global sustainability. It is incumbent upon the agricultural and manufacturing industries to work together to achieve this vision. In particular, the hemp plant has been identified as an emerging industrial crop that will be pivotal in achieving the United Nations Sustainable Development Goals. However, this nascent industry has received an influx of research and development activity, resulting in various methods and practices globally, challenging the repeatability of results, research advancement, standards development, and sustainability assessment. A systematic literature review is conducted to identify and document (1) the various practices for harvesting and converting industrial hemp into materials and products and (2) existing hemp-derived products and those under development. Using the PRISMA methodology, 5295 articles were identified, and 109 articles were included for review. Unlike prior reviews focusing on specific hemp plant components, materials, or products, this study systematically evaluates the utilization pathways of the whole plant (stalk, flower, leaf, and seed) to traditional, industrial, and emerging products. Further, myriad opportunities for hemp material and product applications, sustainability performance assessment, and future research are discussed. This review will benefit future hemp research, advancing process technologies, developing novel products, establishing policies and standards, and assessing sustainability performance. Full article

A typical orchard’s mechanical operation consists of three or four stages: lining and digging for plantation, moving the seedling from nurseries to the farm, moving the seedling to the planting hole, and planting the seedling in the hole. However, the digging of the planting hole is the most time-consuming operation. In fruit orchards, the use of robots is increasingly becoming more prevalent to increase operational efficiency. They offer practical and effective services to both industry and people, whether they are assigned to plant trees, reduce the use of chemical fertilizers, or carry heavy loads to relieve staff. Robots can operate for extended periods of time and can be highly adept at repetitive tasks like planting many trees. The present study aims to identify the locations for planting trees in orchards using geographic information systems (GISs), to develop an autonomous drilling machine and use the developed robot to open planting holes. There is no comparable study on autonomous hole planting in the literature in this regard. The agricultural mobile robot is a four=wheeled nonholonomic robot with differential steering and forwarding capability to stable target positions. The designed mobile robot can be used in fully autonomous, partially autonomous, or fully manual modes. The drilling system, which is a y-axis shifter driven by a DC motor with a reducer includes an auger with a 2.1 HP gasoline engine. SOLIDWORKS 2020 software was used for designing and drawing the mobile robot and drilling system. The Microsoft Visual Basic.NET programming language was used to create the robot navigation system and drilling mechanism software. The cross-track error (XTE), which determines the distances between the actual and desired holes positions, was utilized to analyze the steering accuracy of the mobile robot to the drilling spots. Consequently, the average of the arithmetic means was determined to be 4.35 cm, and the standard deviation was 1.73 cm. This figure indicates that the suggested system is effective for drilling plant holes in orchards. Full article

Open-ventilated greenhouses have reasonable setup costs and low operational costs for growers, which is crucial and most appealing for this research. These attributes fit developing nations like Thailand and other tropical regions. It is challenging to control the equipment intended to obtain an ideal microclimate. This research was conducted in an actual greenhouse setting for data collection and experiments, with a proposed system for adaptive equipment control via web integration. Also, the proposed multivariate multistep LSTM was forecasted over 1 h and cooperated with sensor data. Additional sensors, like a leaf wetness sensor and a CO2 sensor, were installed for detecting plant-level precision for vaporization, rather than greenhouse-level. The proposed system can optimize the indoor temperature within 34.5 to 36 °C with a 39 to 40 °C outdoor temperature. Also, humidity was still at the ideal level of 68 to 70%; more precisely, the wetness value was below 300 throughout the experiment. The model accuracy achieved a sufficient RMSE (0.49) and R² (0.9788). This proposed system architecture and MM-LSTM model has potential as one dimension of a fully smart greenhouse system development in open-ventilated greenhouse settings in tropical regions and Southeast Asian nations for a better yield rate and less human interaction. Full article

Correctly identifying and classifying food is decisive in food safety. The food sector is constantly evolving, and one of the technologies that stands out is augmented reality (AR). During practical studies at Companhia de Entreposto e Armazéns Gerais de São Paulo (CEAGESP), responsible for the largest food storage in South America, difficulties were identified in classifying aromatic herbs due to the large number of species. The project aimed to create an innovative AR application called ARomaticLens to solve the challenges associated with identifying and classifying aromatic herbs using the design science research (DSR) methodology. The research was divided into five stages according to the DSR methodology, from surveying the problem situation at CEAGESP to validating the application through practical tests and an experience questionnaire carried out by CEAGESP specialists. The result of the study presented 100% accuracy in identifying the 18 types of aromatic herbs studied when associated with the application’s local database without the use of an Internet connection, in addition to a score of 8 on a scale of 0 to 10 in terms of the usability of the interface as rated by users. The advantage of the applied method is that the app can be used offline. Full article

Ecuador is one of the world’s leading banana exporters; however, low productivity resulting from inadequate agronomic management requires an analysis of banana production units. This study aimed to define the types of banana production units based on the different agronomic management practices adopted by producers in two Ecuadorian provinces. Data from the National Institute of Statistics and Censuses (INEC) for 2021 were used, with a sample of 319 production units. Principal component and cluster analyses were applied to identify the different types of production units, resulting in four types: high technology conventional (Cluster 1), balanced conventional (Cluster 2), intensive conventional (Cluster 3), and agroecological (Cluster 4). It is important to highlight that 58% of the production units are intensive conventional and use an average of 3.5 management practices, with 98% using fertilizers, 100% using fungicides and pesticides, and 45% using improved genotypes. In contrast, agroecological production is still incipient in Ecuador (4.7%). Regression analysis showed that waste is important in high-yield production units in the three clusters. In addition, Cluster 2 relied on regional factors, family labor, and irrigation efficiency, while in intensive conventional farms (Cluster 3), banana yield was related to fungicide application. Therefore, public policies should be customized according to cluster-specific characteristics to optimize agronomic management practices and facilitate their transfer among groups. Full article

High particulate matter levels in cage-free (CF) houses have led to concerns from producers, as that can pose significant risks to the health and well-being of hens and their caretakers. This study aimed to assess the effectiveness of an electrostatic particle ionization (EPI) + bedding management (BM) treatment in reducing particulate matter (PM) concentrations. Four identical CF rooms each housed 175 hens for six weeks, with two rooms assigned to the EPI + BM treatment (EPI + 20% wood chip topping over 81-week-old litter) and the other two as controls. Measurements of PM were conducted twice a week for 10 min using TSI DustTrak. Additionally, small and large particle concentrations were monitored continuously using a Dylos monitor, with a sampling period of one minute. Footpad scoring was recorded for logistic analysis. Statistical analysis was performed using ANOVA with the Tukey HSD method (p < 0.05). Results demonstrated that the EPI + BM treatment significantly reduced particle counts (37.83% decrease in small particles, 55.90% decrease in large particles) compared to the control group (p < 0.01). PM concentrations were also substantially lowered across different size fractions, ranging from 58.41% to 64.17%. These findings underscore the effectiveness of the EPI + BM treatment in reducing PM in CF houses. The integration of EPI and bedding management innovated in this study holds promise for improving air quality and contributing to the well-being of hens and caretakers in CF housing systems. Full article

This paper presents an actuator fault-tolerant control (FTC) strategy for a hexacopter unmanned aerial vehicle (UAV) designed specifically for precision agriculture applications. The proposed approach integrates advanced sensing techniques, including the estimation of Near-Infrared (NIR) reflectance from RGB imagery using the Pix2Pix deep learning network based on conditional Generative Adversarial Networks (cGANs), to enable the calculation of the Normalized Difference Vegetation Index (NDVI) for health assessment. Additionally, trajectory flight planning is developed to ensure the efficient coverage of the targeted agricultural area while considering the vehicle’s dynamics and fault-tolerant capabilities, even in the case of total actuator failures. The effectiveness of the proposed system is validated through simulations and real-world experiments, demonstrating its potential for reliable and accurate data collection in precision agriculture. An NDVI test was conducted on a sugarcane crop using the estimated NIR to assess the crop’s condition during its tillering stage. Therefore, the main contributions this paper include (i) the development of an actuator FTC strategy for a hexacopter UAV in precision agriculture applications, integrating advanced sensing techniques such as NIR reflectance estimation using deep learning network; (ii) the design of a flight trajectory planning method ensuring the efficient coverage of the targeted agricultural area, considering the vehicle’s dynamics and fault-tolerant capabilities; (iii) the validation of the proposed system through simulations and real-world experiments; and (iv) the successful integration of FTC scheme, advanced sensing, and flight trajectory planning for reliable and accurate data collection in precision agriculture. Full article

Identifying bird numbers in hostile environments, such as poultry facilities, presents significant challenges. The complexity of these environments demands robust and adaptive algorithmic approaches for the accurate detection and tracking of birds over time, ensuring reliable data analysis. This study aims to enhance methodologies for automated chicken identification in videos, addressing the dynamic and non-standardized nature of poultry farming environments. The YOLOv8n model was chosen for chicken detection due to its high portability. The developed algorithm promptly identifies and labels chickens as they appear in the image. The process is illustrated in two parallel flowcharts, emphasizing different aspects of image processing and behavioral analysis. False regions such as the chickens’ heads and tails are excluded to calculate the body area more accurately. The following three scenarios were tested with the newly modified deep-learning algorithm: (1) reappearing chicken with temporary invisibility; (2) multiple missing chickens with object occlusion; and (3) multiple missing chickens with coalescing chickens. This results in a precise measure of the chickens’ size and shape, with the YOLO model achieving an accuracy above 0.98 and a loss of less than 0.1. In all scenarios, the modified algorithm improved accuracy in maintaining chicken identification, enabling the simultaneous tracking of several chickens with respective error rates of 0, 0.007, and 0.017. Morphological identification, based on features extracted from each chicken, proved to be an effective strategy for enhancing tracking accuracy. Full article

The discrete element method (DEM) has been extensively utilized to investigate the mechanical properties of granules, particularly their microscopic behavior, overcoming limitations in field tests such as cost, time consumption, and soil condition restrictions. To ensure the development of reliable DEM simulations, proper contact model selection and parameter calibration are essential. In this research, a DEM parameter calibration method that could represent the nonlinear relationship between clayey soil pressure and sinkage at different moisture contents was proposed. Firstly, the sinking modulus K and the soil deformation exponent n were identified to reflect the nonlinear pressure–sinkage relationship. Then, sensitive DEM parameters on the soli pressure–sinkage relationship were investigated and calibrated, and the effect of moisture content on them was explored. Finally, the transfer of soil internal stress during subsidence was analyzed using the constructed discrete element model. The average error of the sinking modulus K and the soil deformation exponent n between the DEM and the experimental result at four moisture contents were 4.7% and 4.9%, respectively. The relative error of soil internal stress between simulation and experiment was 6.7%, 4.4%, and 9.7% at depths of 50 mm, 100 mm, and 150 mm, respectively. The soil particle trajectory, soil internal stress distribution, and variations during plate pressure–sinkage progress were analyzed by the constructed DEM model. The results demonstrated good agreement with theoretical models and experimental findings. The proposed clayey soil DEM modeling process that considers the pressure–sinkage nonlinear relationship at different moisture contents can be applied in machine-soil research. Full article

The use of aquaponic systems has grown in recent years, but few of these systems have integrated the production of prawns and short-cycle vegetables. This study evaluated the potential for producing microgreens (beet, amaranth, arugula, and red cabbage) integrated with Amazon River prawns (Macrobrachium amazonicum) in an aquaponic system. Four seeding densities (5, 10, 15, or 20 seeds/cell) were assessed in two treatments: one using prawn wastewater and the other using plain dechlorinated water (control). Water quality, prawn growth performance, and microgreen productivity were monitored over 13 days, revealing optimal conditions for both prawns and microgreens in the aquaponic system. Amaranthus paniculatus yielded 374.00 g/m² in prawn wastewater compared to 231.34 g/m² in the control, while Beta vulgaris produced 1734.39 g/m² in wastewater versus 1127.69 g/m² in the control. Similarly, Brassica oleracea (2180.69 g/m²) and Eruca sativa (2109.46 g/m²) had higher yields in the prawn aquaponics system. These findings demonstrate that integrating prawn cultivation in aquaponic systems significantly enhances microgreen production compared to traditional methods. This integrated approach not only improves yields but also offers a more sustainable production model. Significant variation in productivity and growth metrics among species treatments underscores the viability and need for more systematic aquaponic procedures. Full article

The convective hot-air drying technology can cause physicochemical, nutritional, and organoleptic losses in the mango (Mangifera indica). The present Systematic Review was carried out with the objective of comparing mango dehydration technologies to identify the effects on the physicochemical, nutritional, and organoleptic properties of the fruit. Through a review of published scientific and conference papers in the Scopus database, adjusted to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology, a total of 134 documents dated between 2000 and December 6 of 2022 were obtained; 76 of these documents were finally included in the bibliographic and theoretical analysis. Selection parameters emphasizing the relationship between the articles and the research topic, evidenced by including at least one of three dehydration technologies and the fruit of interest with an experimental or theoretical approach to the dehydration subject; review articles and surveys were excluded. Correlation graphs of bibliographic variables were made using the data mining software VantagePoint (version 15.1), which was graphically restructured in Microsoft Excel with the support of statistical analysis. Of the resulting articles, it was found that the countries with authors who participated most in scientific production like India, Brazil, Colombia, the United States, and Thailand, were those related to mango production or importation. Furthermore, the freeze-drying technology allows operating at lower temperatures than convective hot-air drying, contributing to the preservation of ascorbic acid, among other compounds. The refractance window has the shortest operation time to obtain moisture values between 10 and 20%. The dehydrated samples using the refractance window are smooth, homogeneous, non-porous, and comparable to the color obtained with freeze-drying, which is acceptable for industrial applications. Full article

To alleviate soil compaction, subsoiling practices using subsoiler implements are commonly implemented. However, subsoiler bodies are subjected to great draft forces because they work deep in the soil. Therefore, to contribute to draft force reduction, in this work, a bio-inspired attack edge for a subsoiler body based on the internal and external contour lines of the claws of the Mexican ground squirrel (Spermophilus mexicanus) is proposed. As a first step, computational fluid dynamic (CFD) modeling was used to select the best bionic subsoiler (BS) according to the draft force requirements. Then, the BS was fabricated and field-evaluated, and its real draft force during tillage was contrasted with those of a curve subsoiler (CS) and a straight subsoiler (SS). The field evaluation demonstrated that the BS demands, on average, $12.37\%$ and $22.25\%$ less draft force than the CS and SS, respectively. Additionally, the BS was better at entering the soil since its mean tillage depths were $24.86\%$ and $5.73\%$ higher than those of the SS and CS geometries, respectively. Therefore, it was found that modeling the attacking edge of a subsoiler body after the Mexican ground squirrel clearly reduced the draft force during tillage. Full article

The United States is predicted to experience an annual decline in milk production due to heat stress of 1.4 and 1.9 kg/day by the 2050s and 2080s, with economic losses of USD 1.7 billion and USD 2.2 billion, respectively, despite current cooling efforts implemented by the dairy industry. The ability of cattle to withstand heat (i.e., thermotolerance) can be influenced by physiological and behavioral factors, even though the factors contributing to thermoregulation are heritable, and cows vary in their behavioral repertoire. The current methods to gauge cow behaviors are lacking in precision and scalability. This paper presents an approach leveraging various machine learning (ML) (e.g., CNN and YOLOv8) and computer vision (e.g., Video Processing and Annotation) techniques aimed at quantifying key behavioral indicators, specifically drinking frequency and brush use- behaviors. These behaviors, while challenging to quantify using traditional methods, offer profound insights into the autonomic nervous system function and an individual cow’s coping mechanisms under heat stress. The developed approach provides an opportunity to quantify these difficult-to-measure drinking and brush use behaviors of dairy cows milked in a robotic milking system. This approach will open up a better opportunity for ranchers to make informed decisions that could mitigate the adverse effects of heat stress. It will also expedite data collection regarding dairy cow behavioral phenotypes. Finally, the developed system is evaluated using different performance metrics, including classification accuracy. It is found that the YoloV8 and CNN models achieved a classification accuracy of 93% and 96% for object detection and classification, respectively. Full article

Satellite-derived evapotranspiration (ETa) products serve global applications, including drought monitoring and food security assessment. This study examines the applicability of ETa data from two distinct sources, aiming to analyze its correlation with crop yield (rice, maize, barley, soybean). Given the critical role of crop yield in economic and food security contexts, monthly and yearly satellite-derived ETa data were assessed for decision-makers, particularly in drought-prone and food-insecure regions. Utilizing QGIS, zonal statistics operations and time series graphs were employed to compare ETa with crop yield and ET anomaly. Data processing involved converting NRSC daily data to monthly and extracting single-pixel ET data using R Studio. Results reveal USGSFEWS as a more reliable ETa source, offering better accuracy and data continuity, especially during monsoon seasons. However, the correlation between crop yield and ETa ranged from 12% to 35%, while with ET anomaly, it ranged from 35% to 55%. Enhanced collection of satellite-based ETa and crop-yield data is imperative for informed decision-making in these regions. Despite limitations, ETa can moderately guide decisions regarding crop-yield management. Full article

Plant pathologies significantly jeopardise global food security, necessitating the development of prompt and precise diagnostic methods. This study employs advanced deep learning techniques to evaluate the performance of nine convolutional neural networks (CNNs) in identifying a spectrum of phytosanitary issues affecting the foliage of Solanum lycopersicum (tomato). Ten thousand RGB images of leaf tissue were subsampled in training (64%), validation (16%), and test (20%) sets to rank the most suitable CNNs in expediting the diagnosis of plant disease. The study assessed the performance of eight well-known networks under identical hyperparameter conditions. Additionally, it introduced the GamaNNet architecture, a custom-designed model optimised for superior performance on this specific type of dataset. The investigational results were most promising for the innovative GamaNNet and ResNet-152, which both exhibited a 91% accuracy rate, as evidenced by their confusion matrices, ROC curves, and AUC metrics. In comparison, LeNet-5 and ResNet-50 demonstrated lower assertiveness, attaining accuracies of 74% and 69%, respectively. GoogLeNet and Inception-v3 emerged as the frontrunners, displaying diagnostic preeminence, achieving an average F1-score of 97%. Identifying such pathologies as Early Blight, Late Blight, Corynespora Leaf Spot, and Septoria Leaf Spot posed the most significant challenge for this class of problem. Full article

The production of lettuce has increased significantly due to the use of hydroponic systems that rely on substrates. Disposal and acquisition costs present problems, necessitating the identification of sustainable alternatives. The present study aimed to evaluate the use of Eichhornia crassipes (water hyacinth) dry matter in a substrate for the formation of lettuce seedlings. Water plants were collected to obtain their dry matter, and twelve mixtures were formed with Sphagnum and perlite. Mixtures with more water hyacinth dry matter exhibited greater water retention. However, these mixtures also lost water at a faster rate than those containing primarily Sphagnum dry matter did. Higher percentages of germination were detected in the mixtures with water hyacinth dry matter, but these seedlings also presented higher concentrations of proline, such as 16.0 µg mL⁻¹. The mixtures with water hyacinth dry matter presented the highest ion concentrations, mainly at high levels of humidity. Mixtures with a high proportion of water hyacinth dry matter had a greater water retention capacity and a high percentage of lettuce seed that germinated. The mixtures with a higher proportion of Sphagnum led to greater root length, greater concentrations of chlorophyll in cotyledonary leaves, and better morphological development of the seedlings. Full article

The field capacity (FC) and permanent wilting point (PWP) are fundamental hydrological properties critical for assessing water availability within soils, rather than direct measures of soil health. Due to the challenges associated with their field measurement, alternative assessment methods are necessary. In this study, global-scale accessible soil data were retrieved from the world soil database called the World Soil Information Service (WoSIS), and artificial neural network (ANN) and gene-expression programming (GEP) algorithms were used to predict soil FC and PWP based on easily obtainable parameters from the database. The best-fit variable combination for FC (longitude, latitude, altitude, sand content, silt content, clay content, and electrical conductivity) and PWP (best-fit FC combination plus pH) modeling was determined. Both ANN and GEP showed greater accuracy than linear-based models in simulating the FC and PWP from the best-fit variables. The mean absolute error (MAE) was reduced by 51.54% for the FC and 56.38% for the PWP by the ANN model, compared with the linear model used in the previous literature. The normalized root mean square error (NRMSE) evaluation indicated that the ANN model performed best for PWP prediction (NRMSE of 19.9%), while the GEP model was superior for FC prediction (NRMSE of 29.9%). Between the ANN and GEP models, the ANN model showed a slightly higher model of interpretability; however, the GEP model exhibited a similar or better ability to avoid large error, based on the error distribution. Overall, our results demonstrated that machine learning is effective in predicting the FC and PWP from easily accessible data from WoSIS, and the GEP model is more preferable for FC and PWP modeling. Full article

Evaluating farm-scale energy used for broiler production can provide insight into how these facilities use energy and allow for seasonal and managerial influences to be evaluated. This study evaluated farm-scale energy consumption for South Carolina broiler production using energy records from 17 broiler and 4 pullet farms. Monthly electric use showed low to moderate correlation (r ranging from 0.476–0.630) with ambient temperature but had limited predictive usefulness. There was no clear pattern in monthly electrical energy use for broiler barns. However, pullet barns were more consistent and could be grouped into seasons. No significant differences (p < 0.05) in annual electric or gas use were observed between farms with generally better equipment and otherwise similar farms, but production type did influence annual electric use. The average annual electrical use was 23.6 kWh m⁻² for farms producing larger birds, 8.7 kWh m⁻² for those producing smaller birds, and 17.0 kWh m⁻² for pullet farms. Electrical energy use accounted for 37% of total energy in broiler barns and 32% of energy in pullet barns. Combined electric and gas consumption averaged 214 MJ m⁻² yr⁻¹ across all farms. These findings help better quantify farm-scale energy used for broiler production and provide benchmark values for energy use. Full article

This study evaluates a solar PV-powered evaporative cooling system for vegetable cooling. The system features dual cooling chambers with two different biomass pads, operating at different temperatures. To assess its potential, the research examines the evolution of temperature and humidity of the cooling chamber, evaporative effectiveness, cooling capacity, coefficient of performance (COP), energy metrics, greenhouse gas emissions, and overall cost. The results show that the system achieved a temperature depression range of 0.22 to 5.2 °C and 0.57 to 10.94 °C for wood shavings and polyurethane foam, respectively, under no-load conditions, while the values were 0.79 to 4.7 °C and 1.22 to 9.88 °C, with average values of 3.09 and 7.0 °C, for the same materials under loaded conditions. Loaded conditions also yielded a cooling capacity of 5.7 to 33.93 W for wood shavings and 8.13 to 75.55 W for polyurethane foam. The cooling efficiency ranged from 19.9 to 96.42% for polyurethane foam and 3.62 to 60% for wood shavings. The system’s COP was higher than that of solar-powered mechanical chillers, ranging from 2.37 to 22.92. The energy production factor was 2.3 to 2.4, with a lifecycle conversion efficiency of 0.5 and an energy payback time of 1.1 and 2.2 years for using polyurethane foam and wood shavings, respectively. The net present value was positive, and the levelized cost of energy was low, at 36.7 to 38.3 NGN/kWh (0.043–0.045 USD/kWh), making it a viable alternative to grid-based energy systems in Nigeria. Additionally, the system offers significant CO₂ mitigation potential, with estimated carbon credits of NGN 65,059 (USD 71.56) and NGN 98,576.49 (USD 108.43) over its lifetime. Full article

Seasonally Dry Tropical Forests (SDTFs) are situated in regions prone to significant water deficits. This study aimed to evaluate and quantify the dynamics and spatial patterns of vegetation and water bodies through the analysis of physical–hydrological indices for a remnant of FTSD between 2013 and 2021. Basal area, biomass, and tree number were monitored in 80 permanent plots located in two areas of an SDTF remnant with different usage histories. To assess vegetation and water resource conditions, geospatial parameters NDVI, NDWIveg, NDWI, and MNDWI were estimated for the period from 2013 to 2021. The observed patterns were evaluated by simple linear regression, principal component analysis (PCA), and principal component regression (PCR). Area 2 presented higher values of basal area, biomass, and number of trees. In area 1, there was an annual increase in basal area and biomass, even during drought years. The NDVI and NDWIveg indicated the vulnerability of vegetation to the effects of droughts, with higher values recorded in 2020. NDWI and MNDWI detected the water availability pattern in the study area. Physical–hydrological indices in the dynamics of tree vegetation in dry forests are influenced by various factors, including disturbances, soil characteristics, and precipitation patterns. However, their predictive capacity for basal area, biomass, and tree number is limited, highlighting the importance of future research incorporating seasonal variability and specific local conditions into their analyses. Full article

Banana (Musa sp.) is one of the world’s most planted and consumed crops. Analysis of plantations using a geospatial perspective is growing in Costa Rica, and it can be used to optimize environmental analysis. The aim of this study was to propose a methodology to identify areas prone to water accumulation to quantify nitrate concentrations using geospatial modeling techniques in a 40 ha section of a banana plantation located in Siquirres, Limón, Costa Rica. A total of five geomorphometric variables (Slope, Slope Length factor (LS factor), Terrain Ruggedness Index (TRI), Topographic Wetness Index (TWI), and Flow Accumulation) were selected in the geospatial model. A 9 cm resolution digital elevation model (DEM) derived from unmanned aerial vehicles (UAVs) was employed to calculate geomorphometric variables. ArcGIS 10.6 and SAGA GIS 7.8.2 software were used in the data integration and analysis. The results showed that Slope and Topographic Wetness Index (TWI) are the geomorphometric parameters that better explained the areas prone to water accumulation and indicated which drainage channels are proper areas to sample nitrate values. The average nitrate concentration in high-probability areas was 8.73 ± 1.53 mg/L, while in low-probability areas, it was 11.28 ± 2.49 mg/L. Despite these differences, statistical analysis revealed no significant difference in nitrate concentrations between high- and low-probability areas. The method proposed here allows us to obtain reliable results in banana fields worldwide. Full article

In open-field agricultural environments, the inherent unpredictable situations pose significant challenges for effective human–robot interaction. This study aims to enhance natural communication between humans and robots in such challenging conditions by converting the detection of a range of dynamic human movements into specific robot actions. Various machine learning models were evaluated to classify these movements, with Long Short-Term Memory (LSTM) demonstrating the highest performance. Furthermore, the Robot Operating System (ROS) software (Melodic Version) capabilities were employed to interpret the movements into certain actions to be performed by the unmanned ground vehicle (UGV). The novel interaction framework exploiting vision-based human activity recognition was successfully tested through three scenarios taking place in an orchard, including (a) a UGV following the authorized participant; (b) GPS-based navigation to a specified site of the orchard; and (c) a combined harvesting scenario with the UGV following participants and aid by transporting crates from the harvest site to designated sites. The main challenge was the precise detection of the dynamic hand gesture “come” alongside navigating through intricate environments with complexities in background surroundings and obstacle avoidance. Overall, this study lays a foundation for future advancements in human–robot collaboration in agriculture, offering insights into how integrating dynamic human movements can enhance natural communication, trust, and safety. Full article

A micro-tiller vibrates severely during the rotary tillage process, which may cause operators to develop white finger disease. However, for most vibration models, the acting force between the soil and the rotary cutter roll was simplified to only a constant or sine curve, which may not describe the whole dynamic. Rotary tillage processes have been simulated based on the smoothed particle hydrodynamics method in this paper. The acting forces of the soil on the cutter roll have been obtained with the simulation model. Four different working conditions were simulated. The average error between the calculated forces and the simulated mean forces is 10.96%, which proves the SPH model. By introducing simulated acting forces into the vibration model, a new vibration model of the micro-tiller, which includes the soil–blade interaction, has been constructed. Time and frequency characteristics were simulated with the new vibration model. The errors between the simulated and tested RMS values are 4.28%, 5.03%, and 6.35% for the engine, cutter roll, and right handle, respectively. Two domain-dominant frequencies were found with the vibration model, namely 44.7 Hz and 257.0 Hz. It is helpful to reveal the whole dynamic map of micro-tillers. Full article

During the industrial pecan shelling process, kernels are often damaged. To address this problem, a study is conducted to experimentally determine improved impactor geometries for end-to-end pecan cracking. Four impactors of varying internal angles (from 30° to 52.5°, in increments of 7.5°) are tested. After cracking, the pecans are passed through an image analysis software designed to detect and measure cracks in their shells. These measurements help classify each pecan into one of four categories: under crack, standard crack, ideal crack, or over crack. Cracked and ideally cracked pecans are preferred for their processability, so the impactor geometries are then evaluated based on their ability to maximize these crack types across the widest impact energy range. For the four impactors tested, the 30° impactor is found to more consistently produce preferred cracks in a larger energy range relative to the other impactors. Full article

The handling of the remnants of rice crops in the field is not an easy operation, and farmers prefer burning, which causes air pollution, smog, and disease. This research reports the development of a novel precision crop seeder by handling the remnants of previous crops through mechanization. The precision seeder performed multiple operations in a single path, viz, chop residues, incorporate into soil, make mini trenches, and sow wheat with fertilizer application. The precision seeder has a 2040 mm working width, and specially designed C-type blades are used to shred the crop residue. A multiple-speed gearbox with a gear ratio of 1:0.52 is installed, with a further set of spur gears with 16, 18, and 20 teeth that provide 225, 250, 310, and 350 RPMs to the main rotor. In the middle of the seeder, after the main rotor shaft, 11 V-shaped trencher plates are fixed on the trencher roller for the making of trenches. The trencher roller is powered by star wheels, which showed good results. A zero-tillage-type sharp tip edge novel seeder unit was developed for the precise placement of seed and fertilizer. Seed and fertilizer were placed into the mini trenches through 11 seeder units through a ground wheel calibration system. The field capacity of the precision seeder was 0.408 ha/h and the operational cost was calculated 40.68 USD/ha. The seeder showed good results, with the production of 5028 kg/ha compared to conventional methods. The precision seeder provides a mechanized solution for wheat sowing with minimal operational costs by enhancing organic matter in soil with 13% more yield. Full article

In data-scarce regions, prudent planning and precise decision-making for sustainable development, especially in agriculture, remain challenging due to the lack of correct information. Remotely sensed satellite images provide a powerful source for assessing land use and land cover (LULC) classes and crop identification. Applying remote sensing (RS) in conjunction with the Geographical Information System (GIS) and modern tools/algorithms of artificial intelligence (AI) and deep learning has been proven effective for strategic planning and integrated resource management. The study was conducted in the canal command area of the Lower Chenab Canal system in Punjab, Pakistan. Crop features/classes were assessed using the Normalized Difference Vegetation Index (NDVI) algorithm. The Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m and Landsat 5 TM (thematic mapper) images were deployed for NDVI time-series analysis with an unsupervised classification technique to obtain LULC classes that helped to discern cropping pattern, crop rotation, and the area of specific crops, which were then used as key inputs for agricultural mechanization planning and resource management. The accuracy of the LULC map was 78%, as assessed by the error matrix approach. Limitations of high-resolution RS data availability and the accuracy of the results are the concerns observed in this study that could be managed by the availability of good quality local sources and advanced processing techniques, that would make it more useful and applicable for regional agriculture and environmental management. Full article

Basil is a highly valued aromatic plant worldwide, and drying is one of the best ways to preserve its leaves. Through a theoretical approach, the microwave drying of basil leaves can be modeled, and the effective diffusivity can be determined. However, using a model that considers moisture diffusion in the presence of intensive microwave energy is crucial. This study proposed a theoretical model to simulate the microwave drying of basil leaves in a thin layer. The model assumed that the material is homogeneous and isotropic, with the effective diffusivity depending on the microwave power. The model was solved numerically and validated with experimental data. The study also examined the effect of the microwave power on the color and bioactive properties during drying. The drying time was reduced by 60%, by increasing the microwave power from 199 W to 622 W. The effective diffusivity was found to be directly proportional to the microwave power. Drying at low powers was found to cause basil pigment degradation. However, drying at a power of 622 W resulted in better preservation of the leaves without browning. Finally, microwave drying negatively affects the bioactive compounds, as the phenolic content and antioxidant capacity in all the powers evaluated were significantly lower than in fresh basil leaves. Full article

Winemaking facilities require specific interior hygrothermal conditions for wine production and aging, often necessitating the use of electromechanical cooling and humidification systems that increase energy consumption costs. This study aimed to assess the potential application of bioclimatic strategies in artisanal wine cellars within the Guadalupe Valley, Baja California, Mexico, using a quantitative theoretical method. Psychrometric charts incorporating estimated and measured meteorological data from the study area were employed to analyze bioclimatic strategies for two key areas of a wine cellar: (1) Production and (2) Aging. Our findings highlight that integrating high thermal mass and shading techniques represents an effective strategy for wine cellar design, offering reduced reliance on active systems and promoting substantial energy savings. This research underscores the viability and benefits of bioclimatic design approaches in enhancing the sustainability and efficiency of wine cellar operations, particularly in regions with specific climatic challenges. like the Guadalupe Valley. Full article

Remotely piloted aircraft (RPAs) have been increasingly used for crop protection in coffee plantations. However, the applications can result in low spray deposition on leaves and higher product losses between rows compared to ground airblast sprayers. This study aimed to evaluate the spray deposition on the coffee canopy and potential losses to the soil during application with an RPA and an airblast sprayer at varying spray volumes. The experiment comprised four spray treatments: RPA at 10 L ha⁻¹ and 20 L ha⁻¹, and airblast sprayer at 200 L ha⁻¹ and 300 L ha⁻¹. Leaf deposition was quantified by measuring a tracer on leaves from the lower and upper parts of the coffee canopy using spectrophotometry. Spray losses to the soil were measured by analyzing tracer residues on Petri dishes positioned within the inter-rows and beneath the coffee canopy. Statistical process control was used to analyze spray deposition quality in the study area. Ground-based airblast spraying resulted in the highest overall canopy deposition, while RPA spraying led to greater losses within the inter-rows. No significant difference was observed in spray runoff beneath the canopy between ground-based and aerial applications. Leaf deposition exhibited random variability across all application methods. Therefore, application stability, control, and spray quality standards were maintained. Full article

Trichoderma is one of the most widely used microorganisms in the biological control of plant pathogens. The techniques for its formulation are well known and are commercially distributed in both solid and liquid presentations based on formulations of its reproductive structures. Currently, agricultural systems integrate this type of fungus as an alternative for sustainable production, and even though its traditional formulation still has important limitations, it has a high potential to be combined with new technologies for the development and innovation of products that improve their effectiveness. In response to this, micro- and nanotechnology are presented as alternatives to technify bioagents, promoting greater resistance, viability, and dissemination for both biomass and metabolites through encapsulation and smart delivery techniques. Some works have been developed to achieve this, especially using ionic gelation, with good results for agriculture. In this work, some generalities of the organism are mentioned, including its most common formulations for agricultural applications, information related to encapsulation systems, and the potential for improvement of biologics represented by biomass microencapsulation. Full article