Search Results (1,327)

Agricultural tractors are equipped with air braking systems to supply and control the braking systems of towed vehicles. This system’s functional and operational characteristics significantly impact the compatibility and speed of the braking system of the tractor–trailer combination and are therefore checked during approval tests. This paper presents a test methodology and a description of the instrumentation and apparatus used to test the air braking systems of tractors under stationary conditions, as required by EU Regulation 2015/68. Sample test results of the trailer air supply system are included, such as checking the system for leaks, checking the pressure at the coupling heads, checking the compressor flow rate and air reservoir capacity, and checking the response time of the tractor control line. Approval authorities and tractor manufacturers can use the work results for quality control or product qualification tests. Full article

The accurate estimation of aboveground biomass (AGB) is essential for monitoring crop growth and supporting precision agriculture. Traditional AGB estimation methods relying on single spectral indices (SIs) or statistical models often fail to address the complexity of vertical canopy stratification and growth dynamics due to spectral saturation effects and oversimplified structural representations. In this study, a unmanned aerial vehicle (UAV) equipped with a 10-channel multispectral sensor was used to collect spectral reflectance data at different growth stages of cotton. By integrating multiple vegetation indices (VIs) with three algorithms, including random forest (RF), linear regression (LR), and support vector machine (SVM), we developed a novel stratified biomass estimation model. The results revealed distinct spectral reflectance characteristics across the upper, middle, and lower canopy layers, with upper-layer biomass models exhibiting superior accuracy, particularly during the middle and late growth stages. The coefficient of determination of the UAV-based hierarchical model (R² = 0.53–0.70, RMSE = 1.50–2.96) was better than that of the whole plant model (R² = 0.24–0.34, RMSE = 3.91–13.85), with a significantly higher R² and a significantly lower root mean squared error (RMSE). This study provides a cost-effective and reliable approach for UAV-based AGB estimation, addressing limitations in traditional methods and offering practical significance for improving crop management in precision agriculture. Full article

The safety of robotics and automation technologies is a significant concern for stakeholders in Industry 5.0. Ensuring cost-effectiveness and inherent safety requires applying the defense-in-depth principle. This paper introduces a novel risk-informed design framework for functional safety, integrating function-centered hazard identification and risk assessment via fault tree analysis (FTA). Demonstrated in the design of a semi-automated agricultural vehicle, the framework begins with a function-centered hazard identification approach (F-CHIA) based on ISO 12100. It examined design intents, identified hazard zones, and conducted task and function identification. Foreseeable functional hazardous situations are analyzed, leading to functional requirements and the identification of relevant directives, regulations, and standards. The F-CHIA outputs inform the functional safety analysis, assessing the required performance level and deriving specific requirements for software, hardware, and human operators using FTA. The functional requirements derived from F-CHIA are more systematic than traditional methods and serve as effective inputs for functional safety analysis in human–robot collaboration applications. The proposed framework enables design teams to focus on enhancing factors that improve functional safety performance levels, resulting in a more thorough and effective safety design process. Full article

Unmanned articulated vehicles play a crucial role in the intelligent mine system and have been extensively investigated and implemented in the fields of mine transportation, agriculture and forestry construction. However, the working environment of articulated wheeled vehicles is harsh and the working conditions are changeable. These conditions are often accompanied by load changes, road interference excitation caused by an unstructured environment and the dynamic nonlinear characteristics of articulated wheeled vehicles. The current research on path tracking control methods suitable for traditional wheeled vehicles does not meet the intelligent operation requirements of articulated wheeled vehicles, and it is necessary to combine the specific working environment and its own specific structural model characteristics. In this paper, a composite barrier function sliding mode control method based on an extended state observer is proposed to solve the problem of modeling uncertainty and unknown external disturbance in the path tracking control of unmanned articulated vehicles. Firstly, the mathematical model of the articulated wheeled working vehicle is built to derive the expected heading angle in the prediction horizon. Then, the strong nonlinear lumped disturbance in articulated dynamics is dynamically estimated by combining the composite nonlinear extended state observer. Afterward, based on the error compensation theory, a composite barrier function sliding mode controller suitable for articulated vehicle path tracking is derived. Finally, through simulation analysis and experimental verification, this method can estimate the strong nonlinear lumped disturbance caused by the structural characteristics of the articulated vehicle, and then compensate for the disturbance of the control quantity to achieve stable, robust and accurate path tracking control. Full article

Agricultural spraying, despite modern technological advances, still poses the problem of downwind spray drift, which contributes to environmental contamination and ecological imbalance, which are critical sustainability concerns. This study investigated the effect of lateral wind on different unmanned aerial vehicle (UAV) spraying systems under semi-controlled conditions, additionally evaluating the impact of four tank-mix adjuvants (drift reduction agents (DRAs)) at varying concentrations on spray effectiveness, droplet size, and deposition compared to water as a control. By examining UAV-specific spray dynamics, this research provides insights into sustainable drift reduction strategies that minimize environmental impacts. For the UAV spraying performance trials, three UAVs with different spraying configurations were tested, TTA M6E, XAG XP2020, and DJI T30, to identify the most effective system for minimizing downwind spray drift. For the DRA effectiveness trials, four commercially available adjuvants were evaluated at different concentrations utilizing the T30 UAV, which was chosen because it produces the highest proportion of fine droplets. The DRA products included an ionic/non-ionic surfactant (DRA No. 1), silicone-based wetting agents (DRA Nos. 2 and 3), and a silicone-based spreader-adhesive (DRA No. 4). This study showed that, among the tested UAV spray systems, M6E and XP2020 performed better in low-wind conditions, while T30 was more suitable for stable target area deposition in windy conditions but produced higher quantities of fine droplets prone to drifting further. Lateral wind contributes significantly to spray drift, as shown by the results, with increased wind speed causing an additional drift of up to 2 m downwind for all systems. The study also showed that all the tested DRAs exhibit the potential to mitigate drift and improve crop coverage, contributing to more efficient resource use and reduced environmental impacts. All the DRA products either reduce the drift distance by up to 3 m or decrease the deposition by up to 67% compared to water. However, DRA No. 1 showed the best results out of all the tested products in terms of drift control, while DRA No. 4 showed the best target area coverage and adequate drift control capabilities. More field research is required to validate the effectiveness in real-life application scenarios. In summary, the following management measures can be used to control droplet drift using UAV spraying systems, in order of importance: selecting a UAV and nozzles that are optimal for the specific requirements of the spraying task, planning applications in correlation with lateral wind speed, and the use of DRAs. Full article

Counting the number of transplanted crops is a crucial link in agricultural production, serving as a key method to promptly obtain information on crop growth conditions and ensure the yield and quality. The existing counting methods primarily rely on manual counting or estimation, which are inefficient, costly, and difficult to evaluate statistically. Additionally, some deep-learning-based algorithms can only crop large-scale remote sensing images obtained by Unmanned Aerial Vehicles (UAVs) into smaller sub-images for counting. However, this fragmentation often leads to incomplete crop contours of some transplanted crops, issues such as over-segmentation, repeated counting, low statistical efficiency, and also requires a significant amount of data annotation and model training work. To address the aforementioned challenges, this paper first proposes an effective framework for farmland segmentation, named MED-Net, based on DeepLabV3+, integrating MobileNetV2 and Efficient Channel Attention Net (ECA-Net), enabling precise plot segmentation. Secondly, color masking for transplanted crops is established in the HSV color space to further remove background information. After filtering and denoising, the contours of transplanted crops are extracted. An efficient contour filtering strategy is then applied to enable accurate counting. This paper conducted experiments on tobacco counting, and the experimental results demonstrated that the proposed MED-Net framework could accurately segment farmland in UAV large-scale remote sensing images with high similarity and complex backgrounds. The contour extraction and filtering strategy can effectively and accurately identify the contours of transplanted crops, meeting the requirements for rapid and accurate survival counting in the early stage of transplantation. Full article

The precise identification of wheat seedlings in unmanned aerial vehicle (UAV) imagery is fundamental for implementing precision agricultural practices such as targeted pesticide application and irrigation management. This detection task presents significant technical challenges due to two inherent complexities: (1) environmental interference from variable illumination conditions and (2) morphological characteristics of wheat seedlings characterized by slender leaf structures and flexible posture variations. To address these challenges, we propose DHS-YOLO, a novel deep learning framework optimized for robust wheat seedling detection under diverse illumination intensities. Our methodology builds upon the YOLOv11 architecture with three principal enhancements: First, the Dynamic Slender Convolution (DSC) module employs deformable convolutions to adaptively capture the elongated morphological features of wheat leaves. Second, the Histogram Transformer (HT) module integrates a dynamic-range spatial attention mechanism to mitigate illumination-induced image degradation. Third, we implement the ShapeIoU loss function that prioritizes geometric consistency between predicted and ground truth bounding boxes, particularly optimizing for slender plant structures. The experimental validation was conducted using a custom UAV-captured dataset containing wheat seedling images under varying illumination conditions. Compared to the existing models, the proposed model achieved the best performance with precision, recall, mAP50, and mAP50-95 values of 94.1%, 91.0%, 95.2%, and 81.9%, respectively. These results demonstrate our model’s effectiveness in overcoming illumination variations while maintaining high sensitivity to fine plant structures. This research contributes an optimized computer vision solution for precision agriculture applications, particularly enabling automated field management systems through reliable crop detection in challenging environmental conditions. Full article

Plastic pollution is becoming an increasingly serious threat to the natural environment. Macroplastics, primarily polyethylene films, pose significant ecological and economic risks, particularly in the agricultural sector. Effective monitoring of their presence is necessary to evaluate the effectiveness of mitigation measures. Conventional techniques for identifying environmental contaminants, based on field studies, are often time-consuming and limited in scope. In response to these challenges, a study was conducted with the primary aim of utilizing unmanned aerial vehicles (UAVs), multispectral cameras, and classification tools to monitor macroplastic pollution. The model object for the study was an industrial compost pile. The performance of four object-oriented classifiers—Random Forest, k-Nearest Neighbor (k-NN), Maximum Likelihood, and Minimum Distance—was evaluated to effectively identify waste contamination. The best results were achieved with the k-NN classifier, which recorded a Matthews Correlation Coefficient (MCC) of 0.641 and an accuracy (ACC) of 0.891. The applied classifier identified a total 37.35% of the studied compost pile’s surface as contamination of plastic. The results of the study show that UAV technology, combined with multispectral imaging, can serve as an effective and relatively cost-efficient tool for monitoring macroplastic pollution in the environment. Full article

Ensuring secure data transmission in agrotechnical monitoring systems using unmanned aerial vehicles (UAVs) is critical due to increasing cyber threats, particularly with the advent of quantum computing. This study proposes the integration of Quantum Key Distribution (QKD), based on the BB84 protocol, as a secure key management mechanism to enhance data security in UAV-based geographic information systems (GIS) for monitoring agricultural fields and forest fires. QKD is not an encryption algorithm but a secure key distribution protocol that provides information-theoretic security by leveraging the principles of quantum mechanics. Rather than replacing traditional encryption methods, QKD complements them by ensuring the secure generation and distribution of encryption keys, while AES-128 is employed for efficient data encryption. The QKD framework is optimized for real-time operations through adaptive key generation and energy-efficient hardware, alongside Lempel–Ziv–Welch (LZW) compression to improve the bandwidth efficiency. The simulation results demonstrate that the proposed system achieves secure key generation rates up to 50 Mbps with minimal computational overhead, maintaining reliability even under adverse environmental conditions. This hybrid approach significantly improves data resilience against both quantum and classical cyber-attacks, offering a comprehensive and robust solution for secure agrotechnical data transmission. Full article

Cotton (Gossypium hirsutum L.), as a significant economic crop, has undergone significant modernization in planting methods, and its smart irrigation management relies heavily on accurate cotton water content (CWC) estimation. Existing ground-based methods for measuring CWC are constrained by their limited scope and high monitoring costs. Although the development of unmanned aerial vehicle (UAV) technology has provided a new opportunity for large-scale CWC measurements, there remains a gap in the study of CWC estimation in cotton using multi-source and multi-stage data. In this study, we used UAV-based data, including texture features, vegetation indices, and a heat index, and applied four machine learning algorithms, i.e., partial least-squares regression (PLSR), support vector regression (SVR), random forest regression (RFR), and extreme gradient boosting (XGB), to estimate CWC. The findings demonstrate that in a single growth stage, the boll setting stage performs the best, and multi-source and multi-stage inputs can improve the accuracy of CWC estimation, with the best performance of XGB (R² = 0.860). Overall, this study highlights that the synergistic use of multi-source and multi-stage data can effectively improve CWC estimation in cotton, suggesting UAV-based data will lead to a brighter future for precision agriculture. Full article

Under the background of “carbon peaking and carbon neutrality”, carbon reduction is not only a realistic need for the high-quality development of the national economy, but also a key way for the sustainable development of enterprises. Cold chain logistics has the characteristics of high energy consumption and high carbon emissions. Fresh distribution requirements of fresh agricultural products may increase carbon emissions in the cold chain distribution process. Based on customer satisfaction and aiming at minimizing carbon emission and comprehensive distribution cost, this paper establishes an optimization model of cold chain logistics distribution routes for fresh agricultural products. First of all, a two-objective optimization model is proposed considering customer satisfaction maximization and comprehensive cost minimization, including fixed cost, fuel cost, carbon emission cost, cargo damage cost, and time window penalty cost. And when constructing customer satisfaction function, we mainly pay attention to the time factor. Secondly, a hybrid ant colony algorithm, which combines improved ant colony algorithm and local search algorithm 3-opt, is designed to solve the model. Thirdly, a hybrid ant colony algorithm is applied to the simulation example of fresh agricultural products distribution through experimental simulation, and the results are compared with the traditional ant colony algorithm and improved ant colony algorithm. Finally, the results reveal that the average total cost of the hybrid ant colony algorithm is lower than that of the traditional ant colony algorithm and the improved ant colony algorithm, achieving nearly 8.159% and 4.622% cost savings, respectively, and in terms of customer satisfaction, the new algorithm is slightly better than the other algorithms. The results show that the method can effectively optimize the cold chain logistics distribution routes of fresh agricultural products, and reduce carbon emission and distribution cost. Full article

Agricultural drought significantly affects crop growth and food production, making accurate drought thresholds essential for effective monitoring and discrimination. This study aims to monitor the threshold ranges for different drought levels of winter wheat during three growth periods using a multispectral Unmanned Aerial Vehicle (UAV). Firstly, based on controlled field experiments, six vegetation indices were used to develop UAV optimal inversion models for the Leaf Area Index (LAI) and Soil–Plant Analysis Development (SPAD) during the jointing–heading period, heading–filling period, and filling–maturity period of winter wheat. The results show that during the three growth periods, the DVI-LAI, NDVI-LAI, and RVI-LAI models, along with the DVI-SPAD, RVI-SPAD, and TCARI-SPAD models, achieved the highest inversion accuracy. Based on the UAV-inversed LAI and SPAD indices, threshold ranges for different drought levels were determined for each period. The accuracy of LAI threshold monitoring during three periods was 92.8%, 93.6%, and 90.5%, respectively, with an overall accuracy of 92.4%. For the SPAD index, the threshold monitoring accuracy during three periods was 93.1%, 93.0%, and 92%, respectively, with an overall accuracy of 92.7%. Finally, combined with yield data, this study explores UAV-based drought disaster monitoring for winter wheat. This research enriches and expands the crop drought monitoring system using a multispectral UAV. The proposed drought threshold ranges can enhance the scientific and precise monitoring of crop drought, which is highly significant for agricultural management. Full article

The agricultural equipment market has the characteristics of rapid demand changes and high demand for machine models, etc., so multi-variety, small-batch, and customized production methods have become the mainstream of agricultural machinery enterprises. The flexible job shop scheduling problem (FJSP) in the context of agricultural machinery and equipment manufacturing is addressed, which involves multiple resources including machines, workers, and automated guided vehicles (AGVs). The aim is to optimize two objectives: makespan and the maximum continuous working hours of all workers. To tackle this complex problem, a Multi-Objective Discrete Grey Wolf Optimization (MODGWO) algorithm is proposed. The MODGWO algorithm integrates a hybrid initialization strategy and a multi-neighborhood local search to effectively balance the exploration and exploitation capabilities. An encoding/decoding method and a method for initializing a mixed population are introduced, which includes an operation sequence vector, machine selection vector, worker selection vector, and AGV selection vector. The solution-updating mechanism is also designed to be discrete. The performance of the MODGWO algorithm is evaluated through comprehensive experiments using an extended version of the classic Brandimarte test case by randomly adding worker and AGV information. The experimental results demonstrate that MODGWO achieves better performance in identifying high-quality solutions compared to other competitive algorithms, especially for medium- and large-scale cases. The proposed algorithm contributes to the research on flexible job shop scheduling under multi-resource constraints, providing a novel solution approach that comprehensively considers both workers and AGVs. The research findings have practical implications for improving production efficiency and balancing multiple objectives in agricultural machinery and equipment manufacturing enterprises. Full article

This study presents an adaptive sliding mode predictive control (ASMPC) algorithm intended to improve the control precision and robustness of path tracking for wheeled agricultural vehicles. Firstly, the kinematics state equations of the vehicle were established based on path tracking errors. Secondly, in order to design the path tracking controller by combining the precision advantage of model predictive control (MPC) algorithm with the robustness advantage of sliding mode control (SMC) algorithm, the sliding mode functions were designed and used as the output equations to establish the kinematics state space model of the vehicle. Thirdly, on the basis of linearization and discretization for the kinematics state space model, the control law of path tracking was obtained using the MPC algorithm. Finally, according to the fuzzy rules designed by the working speed of the vehicle and the curvature of the reference path, the prediction horizon and control horizon of the MPC algorithm were adaptively adjusted to further improve the control precision and robustness of the path tracking system. The results of CarSim and MATLAB/Simulink co-simulation show that the proposed ASMPC algorithm is superior to the traditional SMC algorithm and conventional MPC algorithm in terms of control precision, dynamic performance, and robustness. The results of our field test show that the root mean square (RMS) values of the lateral errors for straight path tracking and curve path tracking do not exceed 2.1 and 8.7 cm, respectively, in the speed range of 1.0 to 3.5 m/s, suitable for field working. The control precision and robustness of the proposed ASMPC algorithm can meet the working requirements of wheeled agricultural vehicles. Full article

This research explores, using discrete element method (DEM) simulations, the behavior of rice seed infiltration into soil when it is deployed via unmanned aerial vehicle (UAV)-mounted systems. Five distinct sowing strategies were analyzed to evaluate their effectiveness in embedding seeds within paddy soil: gravitational drop, centrifugal spreading, airflow propulsion, pneumatic discharge, and pneumatic shooting. A two-step analysis was performed. Initially, the flight dynamics of rice seeds were modeled, and the influence of air and water drag forces were accounted for. Subsequently, soil penetration was simulated with DEM based on the material properties and contact parameters sourced from the existing literature. The results show that the pneumatic methods effectively penetrated the soil, with pneumatic shooting proving to be the most efficient due to its superior impact momentum. Conversely, the methods that failed to penetrate left seeds on the soil surface. These findings demonstrate the necessity to enhance UAV sowing technology to improve penetration depth while maintaining operational efficiency, and they also offer crucial insights for the progress of UAV applications in agriculture. Full article