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.

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.

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.

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.

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.

Biochar application has been proposed as a promising strategy to improve soil functioning, defined as the integrated regulation of water storage, nutrient availability, and biological activity influencing crop productivity and crop performance in water-limited environments. However, its effectiveness depends on soil properties, climatic variability, and dominant processes. This study evaluated the effects of sewage sludge biochar on soil quality, water dynamics, nutrient availability, and bean productivity in sandy soil under rainfed semiarid conditions across two contrasting cropping cycles. A soil quality index (SQI) based on a minimum data set (MDS) derived from principal component analysis (PCA) was used to identify the dominant processes controlling soil functioning under different hydrological regimes. The two cropping cycles corresponded to wetter (Cycle I) and drier (Cycle II) hydrological conditions within the same agricultural year. Biochar application increased soil organic carbon and nitrogen stocks, enhanced phosphorus availability, and improved soil water storage. Despite similar evapotranspiration among treatments, water productivity increased, indicating more efficient conversion of stored soil water into yield. Biological indicators were more responsive during the wetter cycle, whereas physicochemical indicators dominated under drier conditions, revealing a shift in the processes regulating soil functioning. The minimum data set varied between cycles, demonstrating the environmental dependency of the SQI components. Overall, biochar improved soil resilience by enhancing nutrient retention and buffering crop response to water limitation, and the integrative SQI approach effectively captured these functional changes.

Reliable estimation of rice yield is essential for food security planning, climate-resilient agriculture, and informed policy decisions. This review synthesizes recent research on the integration of remote sensing and crop simulation models for rice yield estimation. The analysis shows that optical and Synthetic Aperture Radar (SAR) data are the most commonly used remote sensing sources, with SAR proving especially valuable in monsoon-affected regions due to its ability to provide consistent observations under cloud cover. Among crop simulation models, DSSAT, APSIM, ORYZA, and WOFOST are most frequently applied, either independently or in combination with satellite-derived information. Across the reviewed studies, integrated approaches, particularly those using data assimilation and hybrid modeling, consistently achieve higher accuracy and better spatial representation of yield compared to standalone remote sensing or crop model methods. Despite these advances, limitations related to data availability, model calibration, scale mismatches, and climate-induced uncertainty remain significant. Based on the reviewed evidence, future efforts should focus on developing practical hybrid frameworks, improving multi-sensor data fusion, and designing scalable systems suited to data-limited regions. Overall, integrating remote sensing with crop simulation models offers a robust pathway for improving rice yield forecasting and supporting climate-adaptive agricultural management.

To address the high energy consumption and low efficiency in shredding film–stalk mixtures during the resource utilization of cotton-field residues in Xinjiang—issues arising from the large mechanical-property differences among the mixture components—a custom single-support shearing fixture was developed to investigate the effects of residual-film wrapping layers, blade rake angle, sliding-cutting angle, and shearing speed on the Fjmax. Based on a Box–Behnken response surface design combined with analysis of variance and microscopic observations of the shearing process, the results showed that all main-effect factors had extremely significant influences on the Fjmax (p < 0.0001). Their relative contributions followed the following order: number of film wrapping layers > blade rake angle > shearing speed > sliding-cutting angle. Residual-film wrapping markedly increased shear resistance; increasing the sliding-cutting angle effectively reduced the shearing force; and reducing the rake angle facilitated more energy-efficient shredding. Interaction analysis further revealed significant coupling between sliding-cutting angle and shearing speed, rake angle and sliding-cutting angle, and rake angle and shearing speed (p < 0.05). Comparative shearing tests indicated that pure cotton stalks exhibited continuous brittle fracture with relatively stable force–displacement profiles, whereas film–stalk composites showed a sequentially coupled failure mode characterized by “residual-film pre-shearing–primary stalk fracture–secondary film stretching,” leading to multi-peak fluctuations in the force–displacement curves. Based on response surface optimization and mechanistic analysis, a parameter combination of a 35° rake angle, a 4–8° sliding-cutting angle, and medium-to-low shearing speed is recommended for shredding operations. This study elucidates the shearing and fragmentation mechanisms of film–stalk mixtures, provides theoretical guidance for optimizing key structural and operational parameters of post-recovery equipment, and offers important engineering value for promoting farmland residual-film pollution control and agricultural waste resource utilization.

Accurate wheat yield prediction is essential for ensuring food security and sustainable resource management under the increasing challenges of climate change. This study investigates the integration of unmanned aerial vehicle (UAV)-based multispectral imaging and machine learning (ML) techniques to improve yield forecasting in European wheat cultivars. Field experiments were conducted on 400 sub-plots with varying NPK fertilization regimes and five wheat varieties, monitored across six phenological stages during the 2023 growing season in Vojvodina, Serbia. A DJI Phantom 4 Multispectral UAV collected high-resolution imagery, from which 65 vegetation indices were computed. Using PyCaret’s automated ML framework, 25 regression algorithms were evaluated for yield prediction. Ensemble models, particularly Random Forest, Extra Trees, Gradient Boosting, and LightGBM, consistently outperformed linear and kernel-based approaches. The highest prediction accuracy was achieved with the Random Forest Regressor during full flowering (BBCH 65–69), yielding an R2 of 0.952 and an RMSE of 0.44 t/ha. Results highlight the temporal dynamics of model performance, with optimal predictions occurring during reproductive stages. The findings confirm that UAV-derived multispectral data, coupled with ensemble machine learning, provide a non-invasive, accurate, and computationally efficient method for yield forecasting. This framework has significant potential for supporting precision agriculture, enabling real-time decision-making, and enhancing the resilience of wheat production systems.

Apple leaf disease identification with high precision is one of the main challenges in precision agriculture. The datasets usually have class imbalance problems and environmental changes, which negatively impact deep learning approaches. In this paper, an ablation study is proposed to test three different scenarios: V1, a hybrid balanced dataset consisting of 10,028 images; V2, an imbalanced dataset as a baseline consisting of 14,582 original images; and V3, a 3× physical augmentation approach based on the 14,582 images. The classification performance of YOLOv11x was benchmarked against three state-of-the-art CNN architectures: ResNet-152, DenseNet-201, and EfficientNet-B1. The methodology incorporates controlled downsampling for dominant classes alongside scenario-based augmentation for minority classes, utilizing CLAHE-based texture enhancement, illumination simulation, and sensor noise generation. All the models were trained for up to 100 epochs under identical experimental conditions, with early stopping based on validation performance and an 80/20 train-validation split. The experimental results demonstrate that the impact of balancing strategies is model-dependent and does not universally improve performance. This highlights the importance of aligning data balancing strategies with architectural characteristics rather than applying uniform resampling approaches. YOLOv11x achieved its peak accuracy of 99.18% within the V3 configuration, marking a +0.62% improvement over the V2 baseline (99.01%). In contrast, EfficientNet-B1 reached its optimal performance in the V2 configuration (98.43%) without additional intervention. While all the models exhibited consistently high AUC values (≥99.94%), DenseNet-201 achieved the highest value (99.97%) across both V2 and V3 configurations. In fine-grained discrimination, the superior performance of YOLOv11x on challenging cases is verified, with only one incorrect classification (Rust to Scab), while ResNet-152 and DenseNet-201 incorrectly classified eight and seven samples, respectively. Degradation sensitivity analysis under controlled Gaussian noise and motion blur indicated that CNN baseline models maintained stable performance. High minority-class reliability, including a 96.20% F1-score for Grey Spot and 100% precision for Mosaic, further demonstrates effective fine-grained discrimination. Results indicate that data preservation with physically inspired augmentation (V3) is better than resampling-based balancing (V1), especially in terms of global accuracy and minority-class performance.