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The breakthrough of articulated tractors has led to a significant increase in productivity and a cost reduction of agricultural operations. The performance of the tractor implement system depends on the understanding of the tractors’ energy parameters. Fertilizer seeders operate between 6 and 9 km h-1, while seed drills allow higher speeds. Increasing sowing speed improves operational efficiency. However, it is important to adequately set the machinery, to optimize the energy parameters, considering the balance between productivity and sustainability. This study evaluated the energy and operating parameters of a 398kW articulated agricultural tractor in a sowing operation at different speeds. The experiment was conducted in a complete randomized block design. Five theoretical speeds were chosen for the sowing operation (6, 7, 8, 9 and 10 km h-1), with seven replications (35 units). The tractor operating parameters were measured: operational speed, slippage, engine speed, hourly fuel consumption, engine thermal efficiency, specific fuel consumption, drawbar force, drawbar efficiency, fuel consumption per area and field operating capacity. The data were subjected to normality test, when significant, variance analysis. Results showed high field capacity in the highest speed and low fuel consumption per area at the highest speed. [ABSTRACT FROM AUTHOR]

LiDAR sensors have great potential for enabling crop recognition (e.g., plant height, canopy area, plant spacing, and intra-row spacing measurements) and the recognition of agricultural working environments (e.g., field boundaries, ridges, and obstacles) using agricultural field machinery. The objective of this study was to review the use of LiDAR sensors in the agricultural field for the recognition of crops and agricultural working environments. This study also highlights LiDAR sensor testing procedures, focusing on critical parameters, industry standards, and accuracy benchmarks; it evaluates the specifications of various commercially available LiDAR sensors with applications for plant feature characterization and highlights the importance of mounting LiDAR technology on agricultural machinery for effective recognition of crops and working environments. Different studies have shown promising results of crop feature characterization using an airborne LiDAR, such as coefficient of determination (R2) and root-mean-square error (RMSE) values of 0.97 and 0.05 m for wheat, 0.88 and 5.2 cm for sugar beet, and 0.50 and 12 cm for potato plant height estimation, respectively. A relative error of 11.83% was observed between sensor and manual measurements, with the highest distribution correlation at 0.675 and an average relative error of 5.14% during soybean canopy estimation using LiDAR. An object detection accuracy of 100% was found for plant identification using three LiDAR scanning methods: center of the cluster, lowest point, and stem–ground intersection. LiDAR was also shown to effectively detect ridges, field boundaries, and obstacles, which is necessary for precision agriculture and autonomous agricultural machinery navigation. Future directions for LiDAR applications in agriculture emphasize the need for continuous advancements in sensor technology, along with the integration of complementary systems and algorithms, such as machine learning, to improve performance and accuracy in agricultural field applications. A strategic framework for implementing LiDAR technology in agriculture includes recommendations for precise testing, solutions for current limitations, and guidance on integrating LiDAR with other technologies to enhance digital agriculture. [ABSTRACT FROM AUTHOR]

With the rapid advancement of technology, precision agriculture, as a modern agricultural production model, has seen significant progress in recent years. Its widespread adoption is gradually transforming traditional farming methods, providing strong support for the modernization of global agriculture. In particular, the application of positioning technology plays a crucial role in precision agriculture. This paper focuses on an automated agricultural machinery positioning system based on Bluetooth technology. The system uses Bluetooth at the 2.4 GHz frequency for transmission, processing Constant Tone Extension (CTE) and Received Signal Strength Indicator (RSSI) signals collected from blind nodes. The Propagator Direct Data Acquisition (PDDA) algorithm is employed to calculate angle information from CTE signals, while the Two-Ray Ground Reflection Model is applied to manage the correlation between RSSI and distance, making it suitable for outdoor environments. These two types of data are fused for positioning, with an optimized objective function converting the positioning task into an optimization problem. An Adaptive Secretary Bird Optimization Algorithm (ASBOA) is introduced to enhance the accuracy and efficiency of the positioning process. In the simulation, anchor and blind nodes are deployed to simulate a real farm environment. Anchor nodes receive CTE and RSSI signals from blind nodes. Considering that the tags mounted on agricultural machinery are set at a fixed height in real scenarios, the simulation also fixes the tags at this height. We then compare the accuracy of five algorithms in both static and dynamic tracking. The final simulation results indicate that ASBOA achieves satisfactory high-precision positioning, both for static points and dynamic tracking, theoretically meeting the needs for continuous positioning and laying a solid foundation for future field trials. [ABSTRACT FROM AUTHOR]

Boundary contour determination during seedling image segmentation is critical for accurate object detection and morphological characterization in agricultural machine vision systems. The traditional manual annotation for segmentation is labor-intensive, time-consuming, and prone to errors, especially in controlled environments with complex backgrounds. These errors can affect the accuracy of detecting phenotypic traits, like shape, size, and width. To address these issues, this study introduced a method that integrated image features and a support vector machine (SVM) to improve boundary contour determination during segmentation, enabling real-time detection and monitoring. Seedling images (pepper, tomato, cucumber, and watermelon) were captured under various lighting conditions to enhance object–background differentiation. Histogram equalization and noise reduction filters (median and Gaussian) were applied to minimize the illumination effects. The peak signal-to-noise ratio (PSNR) and the structural similarity index measure (SSIM) were used to select the clip limit for histogram equalization. The images were analyzed across 18 different color spaces to extract the color features, and six texture features were derived using the gray-level co-occurrence matrix (GLCM) method. To reduce feature overlap, sequential feature selection (SFS) was applied, and the SVM was used for object segmentation. The SVM model achieved 73% segmentation accuracy without SFS and 98% with SFS. Segmentation accuracy for the different seedlings ranged from 81% to 98%, with a low boundary misclassification rate between 0.011 and 0.019. The correlation between the actual and segmented contour areas was strong, with an R2 up to 0.9887. The segmented boundary contour files were converted into annotation files to train a YOLOv8 model, which achieved a precision ranging from 96% to 98.5% and a recall ranging from 96% to 98%. This approach enhanced the segmentation accuracy, reduced manual annotation, and improved the agricultural monitoring systems for plant health management. The future direction involves integrating this system with advanced methods to address overlapping image segmentation challenges, further enhancing the real-time seedling monitoring and optimizing crop management and productivity. [ABSTRACT FROM AUTHOR]

In this study, the agricultural work stress induced by the noise and vibration of some agricultural machinery was analyzed through electroencephalogram (EEG) measurements. The values of spectral edge frequency (SEF) 95%, relative gamma power (RGP), and EEG-based working index (EWI), utilized as stress indicators, were derived by analyzing the EEG data collected. The EEG analysis revealed that agricultural work stress manifested when participants engaged in agricultural tasks following a period of rest. Additionally, the right prefrontal cortex was identified where the values of SEF95% and RGP increased concurrently with the rise in noise (61.42–88.39 dBA) and vibration (0.332–1.598 m/s2). This study's results are expected to be utilized as foundational data to determine the agricultural work stress felt by farmers during work through EEG analysis in response to changes in noise and vibration. [ABSTRACT FROM AUTHOR]

Manual planting and picking cannot fully meet the large-scale industry of green Sichuan pepper in recent years. It is crucial to accurately segment the field scene of green Sichuan pepper, and then extract the navigation path of agricultural machinery. Field management can be advanced in the intelligence of agricultural machinery in green Sichuan pepper fields. In this study, field images were collected from three planting demonstration bases of green Sichuan pepper in Jiangjin District, Chongqing Municipality, in the various planting stages. A total of 400 images were gathered to divide the dataset and test set, according to a 3:1 ratio. The open-source annotation tool Labelme was utilized to annotate the images. A navigation dataset was constructed between the rows of green Sichuan peppers, followed by data enhancement. Given the complex scenes of a green Sichuan pepper field, a lightweight network, Mobile-Unet, was proposed for the semantic segmentation of five scene types: road, trunk, tree, sky, and background. U-Net network was taken as the base semantic segmentation framework and then MobileNetV2 as the feature extraction network. The last three layers of the original MobileNetV2 network were omitted to adapt MobileNetV2 for semantic segmentation. 8-layer 5-times downsampling structure was then aligned with the U-Net architecture. Additionally, the LeakyReLU activation function was employed in the convolutional units to avoid neuron death during training. After segmentation, a navigation line extraction was then introduced to incorporate dual characteristics of roads and tree trunks. Experimental results demonstrate that the dataset and Dice Loss as the loss function effectively enhanced the prediction accuracy of the model. Compared with the two lightweight networks, Fast-Unet and BiseNet, Mobile-Unet has achieved the higher segmentation accuracy on the test set, with a pixel accuracy of 91.15%, mean pixel accuracy of 83.34%, and mean intersection over union of 70.51%. Compared with U-Net, the recognition accuracy was slightly reduced, but the complexity of the model was significantly reduced, with a 92.17% decrease in the memory occupation, and the inference speed of nearly 10 times faster. Additionally, the tests were conducted on 100 test set images for navigation line extraction. A total success rate of 91% was achieved for the extraction. The average deviation of yaw angle was 2.6° and 6.7°, respectively, to extract the navigation line using road contour and tree trunk features. The accuracy requirements were fully met in the field navigation. The finding can offer a valuable reference to explore the visual navigation in green Sichuan pepper fields. [ABSTRACT FROM AUTHOR]

Green agriculture needs energy saving and emission reduction agricultural machinery, and it is feasible to replace fossil energy with new energy based on electricity to become the power source of agricultural machinery. Agricultural machinery is a kind of non-road mobile machinery working in farmland, which is a complex and changeable unstructured scene, including paddy field, hilly farmland and facility farmland. Agricultural machinery for the operation of the object that is the crop is also a lot, the operation of the link also contains a plowing and planting management and harvesting and other operational aspects, which makes the agricultural machinery operation load changes, to the design of electric agricultural machinery has brought many technical difficulties, coupled with the rural areas of the maintenance capacity of the poor, small farmers poor affordability and other socio-economic factors, resulting in the popularization of the application of electric agricultural machinery there are many problems that need to be solved. The characteristics of new energy agricultural machinery using pure electric, hybrid, solar auxiliary power and fuel cell power were reviewed. Domestic and foreign largescale agricultural machinery manufacturers have begun to actively layout the field of new energy agricultural equipment, but mainly concentrated in the concept machine stage. Endurance is a key factor restricting the development of electric agricultural equipment. The battery life of pure electric agricultural machinery products mainly relies on the increase of battery energy density, and hybrid agricultural machinery products increase the battery life through fuel, solar energy or clean fuel. Electric agricultural equipment products are currently mainly concentrated in the concept machine stage, the real mass production of products is less, in addition to consumer product cognition issues, economy is the main determining factor. Compared with foreign countries, China's electric vehicles and new energy industry chain is complete, the rapid development of electric construction machinery, to provide favorable conditions for the electrification of agricultural equipment, should make full use of advantages, accelerate the development and promotion of electric agricultural equipment. This paper starts with the operation characteristics of agricultural machinery, analyzes the key technical requirements of electric agricultural machinery on battery components, electric drive components, electric drive control, energy management and collaborative control, discusses the technical methods to increase the battery life of electric agricultural machinery, adapt to low speed and high torque and multipower output conditions, and summarizes the relevant technical research progress. It is suggested that the battery life of agricultural machinery can be increased by increasing the battery energy density, increasing the battery charging efficiency and increasing the auxiliary power supply. In terms of electric drive, agricultural machinery operations require low-speed motors with large torque, and the different speed requirements of transfer fields and operations require electric drive systems with large speed ranges. In terms of control, the whole machine energy algorithm and coordinated control of agricultural machinery is the focus of research, compared with electric vehicles, electric agricultural machinery in addition to providing power to the driving parts, but also need to achieve the power output of each working part, so the multi-power output power dynamic regulation algorithm is one of the focuses of research. The application of electric agricultural machinery in facility planting, hilly and mountainous areas and leisure agriculture was forecast. In view of the existing problems, it is pointed out that in the future, research should be carried out in the aspects of high-pass special chassis, high-adaptation three-power system, electrification transformation of operating parts, intelligent electrification technology integration, etc., to improve the comprehensive performance of electric agricultural machinery, reduce the cost of electric agricultural machinery through agricultural machinery subsidies, agricultural machinery multi-functional, “battery bank” construction and other methods, and improve the promotion speed of electric agricultural machinery. [ABSTRACT FROM AUTHOR]

Agricultural and rural modernization has been ever accelerating with the promotion of rural revitalization strategy. The transformation of agriculture to intelligence, precision, and environmental protection has been the main trend of development in recent years. Among them, autonomous navigation of unmanned agricultural machinery has drawn much attention to effectively improve accuracy and efficiency. Path tracking control is one of the key technologies in the autonomous navigation of agricultural machinery. A lot of research has been done under relatively ideal working conditions. However, agricultural machinery has to operate in complex and various soil conditions, as well as highly unstructured farmland environments, such as muddy paddy fields, rugged slopes, and slippery grasslands. Agricultural machinery can inevitably encounter various uncertain disturbances during operation in these scenarios, leading to the low accuracy of path tracking and the stability of the control system. Therefore, anti-disturbance and strong self-adaptability are required in the autonomous navigation control of agricultural machinery. This study aims to improve the path following control accuracy and antidisturbance ability of four-wheel self-steering (4WSS) electrically driven unmanned agricultural machinery in complex farmland environments. A control system of non-singular fast adaptive terminal sliding mode was also proposed using a nonlinear disturbance observer. Firstly, a 4WSS chassis extended kinematic tracking error model of unmanned agricultural machinery was established to consider the influence of drive wheel slip on the chassis motion in the farmland environment, including unknown sideslip speed and steering slip angle. Secondly, a nonlinear disturbance observer was designed to estimate the external lumped disturbance with unknown slip parameters, according to the chassis extended kinematic tracking error model. The disturbance compensation was introduced into the controller. A nonlinear adaptive sliding mode was also constructed to slow down the jitter problem of the nonsingular fast terminal sliding mode, according to the exponential approximation. The controller outputs confirmed the acceptable smooth control amount of the steering actuator. The stability of the composite controller was verified at the same time. Once the 4WSS unmanned agricultural machine tracked the U-shaped target path at 0.4 and 0.6 m/s, the standard deviation in the lateral error of the target operation path was reduced to 8.1 and 9.2 cm at different speeds, respectively, compared with the traditional. Furthermore, the standard deviation of lateral error was reduced to 5.3 and 7.8 cm, respectively, in spray mode. The robustness and accuracy of the path tracking control of 4WSS unmanned agricultural aircraft were effectively improved in the complex farmland environment. Better anti-disturbance and strong adaptive ability were achieved to meet the needs of agricultural production. [ABSTRACT FROM AUTHOR]

Levelling and stability of agricultural machinery can be required in hilly and mountainous areas. In this study, an electromechanical machine was designed to combine with the omnidirectional levelling control system using BP neural network PID. Firstly, the structure scheme of the electromechanical machine was provided, including an upper working platform, a middle omnidirectional leveling, and a lower tracked driving. The electromechanical leveling system was achieved in the maximum leveling angles of 20° and 25° in the lateral and longitudinal directions, respectively. Secondly, the angle error leveling was used for the omnidirectional leveling system in the control principle. A mathematical model of the servo-electric cylinder was established for the servo motor and transmission. Then, the BP neural network PID algorithm was designed for the leveling control, in which the weight coefficients of the BP neural network were dynamically adjusted, according to the error of body pitch angle. The PID parameters were optimized to improve the leveling speed, accuracy, and stability of the electromechanical machine. Furthermore, a servo-electric cylinder model was established for the BP neural network PID controller in Matlab/Simulink. An integrated mechanical model of the entire machine was established in Adams software. The simulation and experiment were conducted to compare the leveling control performance of improved and classical algorithms using the Matlab/Simulink-Adams co-simulation platform. A classical PID control algorithm was used as a reference. The simulation results show that the better leveling control performance of the BP neural network PID control algorithm was achieved in the lateral leveling control with a tilt angle of 20° and the longitudinal leveling control with a tilt angle of 25°, compared with the classical one. The average reduction in the lateral and longitudinal leveling rise time was 20.09%, and the average reduction in the leveling stabilization time was 18.99%. The steady-state error was controlled within 0.30°. Finally, the static and dynamic tests were conducted on the physical prototype. The static test results show that the BP neural network PID control shared a shorter time of levelling stabilization, smaller maximum inclination overshoot, and lower steady-state error in both lateral 20° and longitudinal 25° levelling control, compared with the PID control, the differences in the leveling stabilization time were 0.5, and 0.4 s, respectively, and the differences in the maximum inclination overshoot were 1.0°, and 0.5°, respectively. The differences in the steady-state error were 0.20°, and 0.15°, respectively, indicating the errors within a small range. The dynamic test results show that the electromechanical working machine was efficiently operated at a speed of 3 km/h on hilly terrain with uneven surfaces when the inclination angle of the fuselage changed significantly. Compared with the PID control, the BP neural network PID control reduced the average stabilization time by 0.5 s for both longitudinal and lateral leveling, resulting in a higher leveling speed. Additionally, the errors in the longitudinal and lateral leveling angles were within ±1.5°, indicating the higher leveling accuracy. The effectiveness of the leveling system and the BP neural network PID control algorithm was verified through experiments. The automatic leveling control of the working platform was realized to fully meet the requirements of levelling performance for the hilly and mountainous operations. The automatic leveling technology of agricultural machinery was avoided the work quality that caused by uneven road surfaces, and large slopes. The finding can also provide a practical reference to explore the structure design and leveling control of the omnidirectional leveling system in the electromechanical working machine, particularly for agricultural machinery. [ABSTRACT FROM AUTHOR]

Maize threshing is a crucial process in grain production, and optimising it is essential for reducing post-harvest losses. This study proposes a model-based temporal proximity soft actor-critic (TP-SAC) algorithm to optimise the maize threshing process in the threshing drum. The proposed approach employs an LSTM model as a real-time predictor of threshing quality, achieving an R2 of 97.17% and 98.43% for damage and unthreshed rates on the validation set. In actual threshing experiments, the LSTM model demonstrates an average error of 5.45% and 3.83% for damage and unthreshed rates. The LSTM model is integrated with the TP-SAC algorithm, acting as the environment with which the TP-SAC interacts, enabling efficient training with limited real-world data. The TP-SAC algorithm addresses the temporal correlation in the threshing process by incorporating temporal proximity sampling into the SAC algorithm's experience replay mechanism. TP-SAC outperforms the standard SAC algorithm in the simulated environment, demonstrating better sample efficiency and faster convergence. When deployed in actual threshing operations, the TP-SAC algorithm reduces the damage rate by an average of 0.91% across different feed rates compared to constant control. The proposed TP-SAC algorithm offers a novel and practical approach to optimising the maize threshing process, enhancing threshing quality. • LSTM model predicts threshing quality with R2 of 97.17% and 98.43%. • TP-SAC reduces damage rate by 1.04% compared to constant control. • TP-SAC outperforms SAC, achieving maximum return of 63.2 in 35 training steps. [ABSTRACT FROM AUTHOR]

In traditional time-domain extrapolation methods, the peak over threshold (POT) model is unable to accurately identify large load cycles in the load time history, resulting in distorted extrapolation results, particularly when addressing non-stationary loads. To address this problem, this paper proposes a time-domain extrapolation method based on the load cycle amplitude (LCA) model. The core of the method involves using load cycle amplitude features extracted from the measured loads as the basis for modelling, rather than extreme turning points based on threshold extraction. This approach prevents the load's time-domain characteristics from compromising the accuracy of the extrapolation results. The case analysis results demonstrate that the extrapolation method based on the LCA model achieves more reliable results with both non-stationary and stationary loads. Furthermore, the streamlined modelling process results in reductions of 10.63% and 20.84% in the average computing time for the algorithm when addressing stress and vibration loads, respectively. The LCA model proposed in this paper further facilitates the integration of time-domain extrapolation methods into reliability analysis software. [ABSTRACT FROM AUTHOR]

In this study, to compensate for the constraints of high unit cost of portable emission measurement system (PEMS) and measurement environment, we developed a tractor operation-based emission prediction model. We also evaluated the developed prediction model using validation metrics. In addition to engine load data, correlation analysis was conducted on engine temperature and fuel consumption variables. The results showed a high correlation of more than 0.5 between emissions and engine temperature, and a high correlation of more than 0.5 between emissions and fuel consumption for emissions except CO and THC. The R2 values of the CO, THC, NOx, and PM emission prediction models were 0.81, 0.82, 0.85, and 0.97, respectively, showing good overall predictive performance. The prediction models for CO, THC, NOx, and PM emissions developed using the third-order regression analysis all showed excellent performance with an average absolute percentage error of around 2%. Therefore, the developed emission regression model can be used to predict tractor emissions using various variables. Through the exhaust emissions prediction model developed in this study, eco-friendly technology according to the optimal engine design is expected to increase. In addition, it is expected that agricultural machinery prices will be stabilized and export competitiveness will be secured. [ABSTRACT FROM AUTHOR]

To address the challenges posed by the large scale of agricultural machinery trajectory data and the complexity of actual movement trajectories, this paper proposes a two-stage joint clustering method for agricultural machinery trajectory recognition to enhance accuracy and robustness. The first stage involves trajectory clustering, where the spatial distribution characteristics of agricultural machinery trajectories are analyzed, and the position coordinates and the number of neighboring points of trajectory points are extracted as features. The silhouette coefficient method is used to determine the optimal number of clusters k for the K-Means algorithm, thus reducing the data scale. The second stage focuses on trajectory recognition, where a list of Eps and Minpts parameters is generated based on the statistical properties of the trajectory dataset. The Genetic Algorithm is employed for parameter optimization to determine the optimal DBSCAN parameters, enabling precise identification of field operation trajectories and road travel trajectories. Experimental results show that this method achieves mean values of 91.55% for Accuracy, 95.41% for Precision, 89.86% for Recall, and 92.41% for F1-score on a sample dataset of 337 trajectories, representing improvements of 12.8%, 5.13%, 7.79%, and 6.84%, respectively, over the traditional DBSCAN algorithm. Additionally, the Runtime of the two-stage joint clustering method is approximately 30% shorter than that of single-stage clustering. Compared with mainstream deep learning models such as LSTM and Transformer, this method delivers comparable recognition accuracy without the need for labeled data training, significantly reducing recognition costs. The proposed method achieves accurate and robust recognition of agricultural machinery trajectories and holds broad application potential in practical scenarios. [ABSTRACT FROM AUTHOR]

To satisfy the obstacle avoidance requirements of unmanned agricultural machinery during autonomous operation and address the challenge of rapid obstacle detection in complex field environments, an improved field obstacle detection model based on YOLOv8 was proposed. This model enabled the fast detection and recognition of obstacles such as people, tractors, and electric power pylons in the field. This detection model was built upon the YOLOv8 architecture with three main improvements. First, to adapt to different tasks and complex environments in the field, improve the sensitivity of the detector to various target sizes and positions, and enhance detection accuracy, the CBAM (Convolutional Block Attention Module) was integrated into the backbone layer of the benchmark model. Secondly, a BiFPN (Bi-directional Feature Pyramid Network) architecture took the place of the original PANet to enhance the fusion of features across multiple scales, thereby increasing the model's capacity to distinguish between the background and obstacles. Third, WIoU v3 (Wise Intersection over Union v3) optimized the target boundary loss function, assigning greater focus to medium-quality anchor boxes and enhancing the detector's overall performance. A dataset comprising 5963 images of people, electric power pylons, telegraph poles, tractors, and harvesters in a farmland environment was constructed. The training set comprised 4771 images, while the validation and test sets each consisted of 596 images. The results from the experiments indicated that the enhanced model attained precision, recall, and average precision scores of 85.5%, 75.1%, and 82.5%, respectively, on the custom dataset. This reflected increases of 1.3, 1.2, and 1.9 percentage points when compared to the baseline YOLOv8 model. Furthermore, the model reached 52 detection frames per second, thereby significantly enhancing the detection performance for common obstacles in the field. The model enhanced by the previously mentioned techniques guarantees a high level of detection accuracy while meeting the criteria for real-time obstacle identification in unmanned agricultural equipment during fieldwork. [ABSTRACT FROM AUTHOR]

Aimed to improve the quality of picked tea leaves and the efficiency of tea harvesting, an adaptive oolong tea harvesting robot with an adjustment module of a cutting tool and a harvesting line localization algorithm is proposed. The robot includes a vision measurement module and an adjustment mechanism of a cutting tool, enabling it to assess the shape of tea bushes and adaptively adjust the cutter configuration. To address the challenges of complex tea bush structures and environmental noise, a Prior–Tukey RANSAC algorithm was proposed for accurate harvesting model fitting. Our algorithm leverages prior knowledge about tea bush stem characteristics, uses the Tukey loss function to enhance robustness to outliers, and incorporates workspace constraints to ensure that the cutting tool remains within feasible operational limits. To evaluate the performance of the robot, experiments were conducted in a tea garden in Wuyi Mountain, China. Under ideal conditions, our algorithm achieved an inlier ratio of 43.10% and an R2 value of 0.9787, significantly outperforming traditional RANSAC and other variants. Under challenging field conditions, the proposed algorithm demonstrated robustness, maintaining an inlier ratio of 47.50% and an R2 value of 0.9598. And the processing time of the algorithm met the real-time requirements for effective tea-picking operations. The field experiments also showed an improvement in intact tea rates, from 79.34% in the first harvest to 81.57% in the second harvest, with a consistent usable tea rate of around 85%. Additionally, the robot had a harvesting efficiency of 260.14 kg/h, which was superior to existing handheld and riding-type tea pickers. These results indicate that the robot effectively balances efficiency, accuracy, and robustness, providing a promising solution for high-quality tea harvesting in complex environments. [ABSTRACT FROM AUTHOR]

Smart agriculture development mainly depends on the intelligence and reliability of autonomous agricultural machinery. Automatic navigation systems (ANSs) play a key role in intelligent agricultural machinery design, as they not only reduce farmers' workloads but also improve their land utilization rates. In this paper, a tractor ANS based on dynamic path search and a fuzzy Stanley model (FSM) was designed, and its capability for whole-field path tracking was tested. First, the tracking performance of the steering control module was validated after the automatic reconstruction of the tractor platform. Then, a navigation decision system was established based on a unified reference waypoint search framework, where the path generation for whole-field coverage was presented. Finally, the gain coefficient of the Stanley model (SM) was adjusted adaptively according to the tracking error by utilizing the fuzzy logic controller. Subsequently, the developed tractor ANS was tested in the field. The experiment's results indicate that the FSM outperformed the SM in straight path tracking and whole-field path tracking. When the tractor traveled at a speed of 1 m/s, the maximum lateral tracking error for the straight path was 10 cm, and the average lateral tracking error was 5.2 cm, showing improvements of 16.7% and 10.3% compared to the SM. Whole-field autonomous navigation showed that the maximum lateral tracking error was improved from 34 cm for the SM to 27 cm for the FSM, a reduction of approximately 20.6%, illustrating the superiority of the FSM in the application of whole-field path tracking. As the maximum tracking error of whole-field autonomous navigation appears in the turning stage, where tractors often stop working, the designed ANS satisfies the requirements of a self-driving system for unmanned tractors. [ABSTRACT FROM AUTHOR]

On 14 June 2023, the European Parliament adopted Regulation (EU) 2023/1230 on machinery, which entered into force on 19 July 2023 (with some exceptions as per art. 54, according to a corrigendum issued to address a clerical error as regards the application dates in the original version) and shall apply from 20 January 2027, replacing the Machinery Directive 2006/42/EC. The main innovations/differences introduced by the Machinery Regulation (MR) compared to the Machinery Directive (MD) are critically analysed here, with a focus on sustainability issues. Some of these issues are covered by several international standards (such as BS 8887, ISO 10987 or DIN 91472), which also define the criteria and requirements for the remanufacturing process, although some technical gaps remain. Using the example of agricultural machinery, this paper proposes a methodology for determining the areas of acceptability for remanufactured products: these are expressed in terms of structural performance (e.g., the number of cycles ahead to failure expressed as the mutual of damage 1 − D = 0.625 ) and the functional and safety requirements of the original machine. In this way, the issue of "substantial modification of machinery" is explored in terms of the safety obligations that the remanufactured machinery must fulfil. The paper is therefore a contribution to circular design by providing general criteria for the extension of the service life of machinery while at the same time considering safety issues. [ABSTRACT FROM AUTHOR]

We employ a neural network (NN) approach—Long Short‐Term Memory (LSTM)—to study agricultural commodity prices during the 2008 Great Recession and the COVID‐19 recession. Our analysis reveals more structural breaks and higher volatility in plant‐based commodities like corn and soybeans during recessions compared with animal‐based commodities. The price reactions varied among commodities, with corn responding first to both recessions, while milk price, which was found independent of other prices, recovered last from the financial recession and first from the disease‐induced recession. This insight into commodity behavior during recessions can aid in trend prediction and recession preparation for investors and researchers. [ABSTRACT FROM AUTHOR]

We develop general equilibrium models that incorporate an agricultural equipment sector to explore the impact of climate change on skilled-unskilled wage inequality in a small open economy, with a specific focus on the role of the domestic capital market. In the model with the agricultural equipment sector using skilled labor as input, we illustrate that climate change decreases both skilled and unskilled wages. However, its effect on wage inequality varies depending on the discrepancy of the capital intensity between the urban skilled and unskilled sectors. Moreover, if the demand for capital in the agricultural equipment sector significantly influences the capital market, this sector will not only act as a buffer, but also trigger a reversal change of wage inequality. To enhance the robustness of our findings, we extend the model to include considerations of public goods provision and taxation. [ABSTRACT FROM AUTHOR]

In addition to improving the efficacy of the furrow opener by ensuring consistent seeding depth, the gryllulus jaw geometry curve was integrated into the furrow opener. Soil particles were modeled using the DEM combined with the Hertz–Mindlin with JKR model, and simulation tests were conducted using the DEM corn stover model. Three geometric curves of gryllulus jaws were extracted. The effect of each curve and magnification on the manipulation results was clarified by the simulation test. Subsequently, field trials were conducted to evaluate the stability of the seeding depth of the bionic structure. The experiment showed that the No. 1 structure with a magnification of 1000 was the best, and the stability was 42.10% higher than that of the original structure. The results of this research can provide key structural and simulation parameters for the development of planter furrow openers with both efficient straw crushing and stable sowing depth functions, which is of great significance for the improvement of agricultural machinery. [ABSTRACT FROM AUTHOR]

Agri‐food supply chains (AFSCs) need to become more sustainable, and Industry 4.0 (I4.0) is a crucial enabler. However, various barriers make adopting I4.0 technologies to achieve AFSC sustainability challenging. Few previous studies have examined China's agri‐food industry. Through a literature review and consultation of Chinese experts, we identify 27 barriers in six categories and prioritize these using a group‐based fuzzy analytic hierarchy process to produce novel results. First, we identify six new I4.0 adoption barriers closely linked with China's economic, social and cultural environments, including acquisition of intelligent agricultural equipment subsidies based on guanxi. Second, our prioritization of barriers reveals that the key challenges to applying I4.0 are the increased cost of terminal logistics, acquisition of intelligent agricultural equipment subsidies based on guanxi, low compatibility of I4.0 technologies with existing agricultural equipment, and problems with the government subsidy model. These results have managerial implications for governments and knowledge dissemination organizations. [ABSTRACT FROM AUTHOR]

In response to the situation in the Huanghuai region, where corn fertilization and pesticide application primarily rely on manual methods such as hand broadcasting fertilizer and using manual backpack sprayers, resulting in low levels of mechanization, this study designed an electric integrated work vehicle in line with the trend of developing new energy. The vehicle is powered by six 12 V, 100 Ah lead-acid batteries and integrates the functions of fertilization and pesticide spraying. It can achieve precise hole-fertilization, applying fertilizer to a depth of 100 to 150 mm near the roots of corn, and can also perform multi-row pesticide spraying. The vehicle's electronic control system is divided into two functional areas: 220 V and 24 V. The walking system uses a 220 V, 2 kW AC servo motor, which is driven by converting the voltage of the 72 V battery group into a 220 V sine wave AC through an inverter, and the motor speed can be adjusted. The working width is adjusted by two fixed electric cylinders at the top of the rear wheel frame. The user can preset the width through the control panel, and during operation, the electric cylinders can be automatically controlled to the optimal working width via a whisker-type limit switch. Analysis using ADAMS software shows that when the vehicle speed is 2, 3, and 5 km per hour, the opening angles of the duckbill controller are 66°, 58°, and 48°, respectively, indicating that the higher the speed, the smaller the opening angle. This shortens the fertilization interval time and makes the fertilization spacing more stable. The maximum opening angle of the adjacent duckbill is 25°, indicating that the fertilization amount remains stable. When the vehicle is moving in reverse, the duckbill always remains closed, and at different speeds, the opening angle change curve of the duckbill controller is smooth and regular. This vehicle significantly improves the efficiency and precision of corn planting. However, improvements are still needed in battery technology, control system optimization, and the high cost of electric agricultural machinery to promote the widespread application of agricultural mechanization. [ABSTRACT FROM AUTHOR]

The advancement of unloading technology in combine harvesting is crucial for the intelligent development of agricultural machinery. Accurately measuring material pile height in transport vehicles is essential, as uneven accumulation can lead to spillage and voids, reducing loading efficiency. Relying solely on manual observation for measuring stack height can decrease harvesting efficiency and pose safety risks due to driver distraction. This research applies binocular vision to agricultural harvesting, proposing a novel method that uses a stereo matching algorithm to measure material pile height during harvesting. By comparing distance measurements taken in both empty and loaded states, the method determines stack height. A linear regression model processes the stack height data, enhancing measurement accuracy. A binocular vision system was established, applying Zhang's calibration method on the MATLAB (R2019a) platform to correct camera parameters, achieving a calibration error of 0.15 pixels. The study implemented block matching (BM) and semi-global block matching (SGBM) algorithms using the OpenCV (4.8.1) library on the PyCharm (2020.3.5) platform for stereo matching, generating disparity, and pseudo-color maps. Three-dimensional coordinates of key points on the piled material were calculated to measure distances from the vehicle container bottom and material surface to the binocular camera, allowing for the calculation of material pile height. Furthermore, a linear regression model was applied to correct the data, enhancing the accuracy of the measured pile height. The results indicate that by employing binocular stereo vision and stereo matching algorithms, followed by linear regression, this method can accurately calculate material pile height. The average relative error for the BM algorithm was 3.70%, and for the SGBM algorithm, it was 3.35%, both within the acceptable precision range. While the SGBM algorithm was, on average, 46 ms slower than the BM algorithm, both maintained errors under 7% and computation times under 100 ms, meeting the real-time measurement requirements for combine harvesting. In practical operations, this method can effectively measure material pile height in transport vehicles. The choice of matching algorithm should consider container size, material properties, and the balance between measurement time, accuracy, and disparity map completeness. This approach aids in manual adjustment of machinery posture and provides data support for future autonomous master-slave collaborative operations in combine harvesting. [ABSTRACT FROM AUTHOR]

Selecting the appropriate tractor for small-scale farms is a complex process due to the multitude of technical, environmental, and economic criteria that must be evaluated. This study addresses this challenge by integrating the Analytic Hierarchy Process (AHP) with machine learning (ML) to reduce the number of criteria and simplify the decision-making process. The research aims to determine the most relevant criteria aligned with sustainable development goals for selecting the right tractor, focusing on small farms in the Egyptian Delta. Four tractors, with horsepower ranging from 55 to 95, were evaluated based on inputs from forty-two governmental service providers in the study area. Initially, nine criteria were identified, encompassing key technical, environmental, and economic factors. These criteria were reduced to three—price, power, and maintenance costs by weights 0.142, 0.334, and 0.525, respectively—using Hierarchical Agglomerative Clustering with Euclidean distance. This reduction streamlined the selection process, making it more practical for farmers. Results show that the second tractor (T2), with a priority score of 0.326 and a normalized value of 33.4%, emerged as the optimal choice for small-scale farmers, outperforming the first tractor (T1) (28.7%) and third tractor (T3) (21%). Integrating AHP and ML simplifies tractor selection, ensuring sustainability, cost-efficiency, and operational effectiveness. [ABSTRACT FROM AUTHOR]

The flower-growing sector in Latin America presents significant health risks for workers, which highlights the need for technological updates in their production processes. Likewise, outdated machinery leads to losses that need to be avoided. The method of productive innovation developed in this document involves optimizing a mechanism of agricultural machinery used in carnation classification. The optimization is achieved by minimizing the jerk of the mechanism's movement using metaheuristic methods. The results of three metaheuristic methods are compared against a brute force methodology. Optimization using these metaheuristic methods allows for achieving satisfactory results with up to 98% time reduction in the optimization process. This jerk optimization gives a longer useful life to the machinery, reduces the production stops needed for maintenance from once an hour to once every three hours, and reduces the damage done by the machine to the carnation stems. [ABSTRACT FROM AUTHOR]

Aiming at the difficulty of controlling the longitudinal relative position of agricultural machines during the agricultural master–slave navigation cooperative operation and the weak adaptability of the unitary traditional control method in the face of the working conditions of complex farmland environments, this paper proposes a supervised reinforcement learning (SRL)-based longitudinal stable and safe control method applicable to master–slave navigation harvesting and unloading operations. Firstly, to improve the algorithm training success rate, a supervisor trained on actual driving data is introduced into the actor–critic reinforcement learning method. Secondly, in order to improve the effect of agricultural machine operation, considering the actual grain unloading operation scene and combining the smoothness of operation and the safety of unloading, a new reward function in the supervised reinforcement learning algorithm is designed. Finally, the performance of the proposed SRL control strategy is verified by simulation and agricultural machines following grain unloading tests. The results of field operation show that, when the harvester speed is 1.2 m/s, the average absolute deviation of the actual distance between the two trucks is 0.048 m, and the maximum deviation of the steady state is 0.26 m. In the variable speed test, when the harvester speed is 0.4 m/s and 1.2 m/s, the average absolute deviation of the actual distance between the two trucks is 0.079 m and 0.091 m, and the maximum deviation of the steady state is 0.20 m and 0.21 m, and the cooperative accuracy can fulfill the operational demands of harvesting cooperative unloading. The study's results can serve as a technological reference for autonomous harvesting operations in the field. [ABSTRACT FROM AUTHOR]

In facility-based agricultural environments, accurately identifying green tomatoes presents a significant challenge for machine vision systems due to the color similarity between green fruits and background branches and leaves as well as the overlapping occlusion between fruits. To solve this problem, this study constructs and optimizes the Attention Gate (AG) module using Swin-Unet as the baseline model, so that the model can focus on the features related to green tomatoes, suppress irrelevant regions in the background, and effectively enhance the representation of target features. Additionally, in order to optimize the edge smoothing of green tomato segmentation, this study further introduces a Atrous Spatial Pyramid Pooling (ASPP) module, which significantly improves the segmentation accuracy by expanding the feature sensing field and enhancing the multi-scale feature extraction capability. Experimental results on the specially constructed green tomato dataset show that the model achieves 97.5%, 92.4% and 85.9% for Pixel Accuracy (PA), Dice similarity coefficient (Dice) and Intersection over Union (IOU), respectively. The new model outperforms existing partial semantic segmentation models in several key metrics, proving its effectiveness in complex facility environments. This research not only addresses the technical difficulties in recognizing green fruits, but also provides solid technical support for the development and application of intelligent agricultural equipment. The model can be applied to segmentation and recognition of other types of fruits to meet the accuracy and efficiency requirements of green fruit recognition in smart agricultural equipment, which has a broad application prospect. [ABSTRACT FROM AUTHOR]

The working environment and objects of agricultural machinery are different from those of automobiles, and agricultural machinery is greatly affected by such working environment and working conditions, while its power output system is more complex. Agricultural machinery not only has drive output, but also PTO output and hydraulic output, which together constitute the output system of agricultural machinery. Agricultural machinery conditions can be divided into road transportation working conditions and field operation working conditions. The working conditions of agricultural machinery can be divided into different load conditions according to the different traction tools and whether the hydraulic and PTO work, such as ploughing, rotary tillage, fertilization and transportation. Therefore, developing hybrid electric agricultural machinery systems that are suitable for various complex working conditions holds great theoretical significance and practical value. This should be done bearing mind the complex working conditions of agricultural machinery systems in agricultural work and the intricate challenges in designing hybrid agricultural machinery systems. In this paper, a two-dimensional matrix is used to represent the physical structure and dynamics of the multi-channel power output agricultural mechanism. A hierarchical two-dimensional matrix method for the generation and screening of hybrid electric agricultural machinery systems with multi-power output power is also proposed. The components of agricultural machinery are divided into an input layer and an output layer, and these components are coded and defined, and then transformed into a matrix. The hierarchical two-dimensional matrix method is used to generate and screen the hybrid electric agricultural mechanism type. Through the stratification of the matrix, complexity of the configuration generation is reduced, and the constraints are applied to the basic screening of the configurations generated. The rationality of the configurations obtained after generation and screening is verified by Simulink simulation. The results show that the configuration screened by this method can meet the performance requirements of agricultural machinery. [ABSTRACT FROM AUTHOR]

Aiming at the operation scenario of existing crop coverage and the need for precise row alignment, the sowing prior navigation information of cotton fields in Xinjiang was used as the basis for the study of path planning for subsequent operations to improve the planning quality and operation accuracy. Firstly, the characteristics of typical turnaround methods were analyzed, the turnaround strategy for dividing planning units was proposed, and the horizontal and vertical operation connection methods were put forward. Secondly, the obstacle avoidance strategies were determined according to the traits of obstacles. The circular arc–linear and cubic spline curve obstacle avoidance path generation methods were proposed. Considering the dual attributes of walking and the operation of agricultural machinery, four kinds of operation semantic points were embedded into the path. Finally, path generation software was designed. The simulation and field test results indicated that the operation coverage ratio CR ≥ 98.21% positively correlated with the plot area and the operation distance ratio DR ≥ 86.89% when non-essential reversing and obstacles were ignored. CR and DR were negatively correlated with the number of obstacles when considering obstacles. When considering non-essential reversing, the full coverage of operating rows could be achieved, but DR would be reduced correspondingly. [ABSTRACT FROM AUTHOR]

The self-propelled straw pickup baler in agricultural work is responsible for collecting and compressing straw to facilitate transportation and storage, while reducing waste and environmental pollution. Like other agricultural equipment, the straw pickup baler is a complex mechanical system. During operation, its excitation characteristics under multi-source stimuli and the coupling characteristics of various components are not yet clear. This paper analyzed the excitation mechanics property of each component of the self-propelled straw pickup baler and established balance equations. Based on the balance equations, the coupling characteristics of the structures were studied. Through experiments collecting excitation signals from multiple devices under different operating conditions, the vibration excitation signals of each component were obtained. The experiments revealed that the excitation and coupling signals in the Z direction are particularly evident. Based on experiments, the effective Z-direction vibration signal value on the left front of the chassis exceeds 7 m·s2, while on the right front it increases from 1.995 m·s2 to 7.287 m·s2, indicating the most intense vibration direction. It was also found that, at the driver's cab, the effective Z-direction vibration signal values at two response points, 11 and 12, both exceed 7 m·s2. The data indicate significant vibrations occur in both the longitudinal and vertical directions. Using the Signal Analyzer module in MATLAB for signal processing, it was found that the prominent filtered signals consist of combustion excitation harmonics and continuous low-frequency vibrations from the compression mechanism. The periodic reciprocating compression motion of the crank-slider mechanism causes sustained impacts on the frame, leading to periodic changes in the vibration amplitude of the chassis. Thus, the vibration reduction of the compression mechanism's periodic motion is key to reducing the overall vibration of the machine. [ABSTRACT FROM AUTHOR]

Accurate measurement of slip rate (SR) in agricultural tractors, particularly in Iraq, is essential for optimizing tractive efficiency, fuel economy, and field efficiency. Presently, tractors in Iraq lack sensors for SR detection, posing a challenge. This research addresses the issue by introducing a wireless technology, the novel digital slippage system (NDSS), designed to precisely measure the SR of rear wheels. The NDSS was tested across diverse field conditions, involving six soil textures and various kinds of agricultural tillage equipment (A-TE). Different tillage practices, including conservational tillage (CT) with a chisel plough, traditional tillage (TT) with a moldboard plough and disc plough, and minimum tillage (MT) using disc harrowing and spring tooth harrowing, were examined. Results from the NDSS were compared to traditional techniques, demonstrating the cost effectiveness and overall performance. Silty loam soil exhibited higher SR values, while the silty clay soil showed lower values. SR varied significantly across soil textures, with more cohesive soils leading to reduced SR percentages. Additionally, tillage methods had a marked influence on SR values. The use of CT resulted in higher SR values of 18.35% compared to TT and MT systems, which recorded lower SR values of 13.69% and 6.03%, respectively. SR measurements were also found to be affected by the draft force during the loading of A-TE, emphasizing the role of operational conditions in tractor performance, especially in challenging field environments. Comparison between NDSS and traditional techniques revealed that the NDSS offered high accuracy, flexibility, configurability, and consistent performance. The NDSS demonstrated superior precision, making it an effective tool for assessing SR in agricultural tractors. [ABSTRACT FROM AUTHOR]

Using farm machinery plays a significant role in easing the issue of slowing growth of operating income among farm households in China. Drawing data from CFPS2018, this study adopts a multinomial endogenous switching regression (MESR) to analyze the factors influencing farm households' choices regarding self-owned farm machinery and outsourced machinery services, as well as their subsequent impact on operating income. The results of the study show that the characteristics of the head of household, family, village, and region have a significant impact on the farm households' selection of whether to use self-owned machinery or outsourced services. Furthermore, the exclusive use of self-owned farm machinery and the combined use of both self-owned and outsourced machinery substantially enhance farm households' operating income. An additional analysis indicates that these two types of machinery are complementary, and their combined use generates a superimposed effect that further boosts income. These findings suggest that the combined use of self-owned and outsourced machinery is optimal for farm households who wish to expand their operating income. [ABSTRACT FROM AUTHOR]

Cassava is one of the world's staple crops and an important economic and food crop for one billion people. It holds significant value in food, feed, and industrial raw materials. However, cassava production is faced with the following problems: agricultural machinery and agronomy are not integrated, work efficiency is not high, it is not suitable for large-scale planting plots, and there is a lack of large-scale equipment, automation, and intelligent cassava production equipment. This study reviews research on cassava planting agronomy and mechanized production technologies and equipment, including planting, stem–tuber separation, and harvesting, both globally and in China. The shortcomings are analyzed, and recommendations are provided for current mechanized cassava production to accelerate the development of intelligent equipment and strengthen the shortcomings. Meanwhile, we used typical and extensive Chinese planting and harvesting machines as case studies. The future trends in cassava production are forecasted, underscoring the importance of advancing global mechanized technology and equipment for cassava production to enhance its efficiency, competitiveness, and modernization. [ABSTRACT FROM AUTHOR]

In this study, a "green composite" material made from 60% tree bark and 40% polylactic acid (PLA) was fabricated and evaluated according to its mechanical properties and biodegradability. Biodegradation tests were performed in compost, simulated aquatic environments, and natural soil. In compost, the composite degraded steadily and reached 47% biodegradation after 11 weeks. In soil, the material quickly lost much of its tensile strength, and after 6 weeks, there were signs that the surface and the internal structure had started to deform. Biodegradation in aquatic environments also caused a loss of tensile strength after only a few weeks. Because of the high filler content, excellent biodegradability, and light weight, the composite material has a low environmental footprint. The material could be used in agricultural equipment such as plant pots. [ABSTRACT FROM AUTHOR]

[Objective] Carbonyl compounds are important precursors of ozone and significantly affect air quality and human health. Diesel vehicles have been identified as a dominant contributor to carbonyl compound emissions. In China, agricultural machines have become an important emission source of off-road mobile pollution because of its increasing number and the backward control of its emission. With worsening atmospheric pollution and depleting petroleum resources, finding alternatives to petroleum is urgent. Biodiesel, mainly referred to as fatty acid methyl ester formed by esterification reactions of vegetable, animal, and waste oils with methanol or ethanol, is regarded as an important clean energy source. In this context, the impact of biodiesel on reducing carbonyl emissions compared with fossil diesel remains unclear, leading to a very large uncertainty in achieving China's goal of "reducing pollution and carbon" for diesel machinery. [Methods] Therefore, five representative agricultural machines (three tractors and two combine harvesters) fueled with different biodiesel and diesel blends--pure fossil diesel (B0), B5 biodiesel, and B20 biodiesel--were selected in this study to characterize carbonyl compound emissions. Real-world measurements were conducted using a dilution system in a rice field in Gongdao Town, Yangzhou City, Jiangsu Province. A total of 17 carbonyl compounds (formaldehyde, methanol, acetaldehyde, formic acid, acrolein, propionaldehyde, acetic acid, butenone, butyraldehyde, valeraldehyde, ethyl acetate, cyclohexanone, hexaldehyde, benzaldehyde, heptaldehyde, methylbenzaldehyde, and caprylaldehyde) were detected using a proton-transfer reaction time-of-flight mass spectrometer (Ionicon Analytik, Innsbruck, Austria). [Results] Overall, biodiesel-fueled engines exhibited significantly higher carbonyl emission factors than diesel-fueled engines, demonstrating the trend of B5 > B20 > B0. Average emission factors for B5, B20, and B0 fueled engines were (1.64 ± 1.04), (1.41 ± 0.84), and (1.18 ± 0.76) g/kg fuel, respectively. These results were consistent with emissions from light-duty diesel trucks (LDDTs) but were higher than those from three- and four-wheel rural vehicles and LDDTs. Propionaldehyde, acetic acid, acrolein, and butyraldehyde were the dominant carbonyl species for the tested agricultural machines, accounting for 71.4%, 73.2%, and 80.1% of the total carbonyls for B0, B5, and B20, respectively. The ozone formation potential (OFP) of carbonyl was higher for B5- and B20-fueled agricultural machines than those fueled by B0, owing to the biodiesel's higher oxygen content than diesel. Furthermore, the OFPs for agricultural machines were higher than those for diesel trucks. In addition, cancer risks from carbonyls were the highest for B5, followed by B20 and B0. [Conclusions] In conclusion, different biodiesel blend ratios, engine types, and operation modes affect carbonyl emissions. Although biodiesel is promoted as an alternative fuel in some cities, its impact on carbonyl emission, air quality, and human health should be carefully addressed owing to its higher oxygen content than diesel. [ABSTRACT FROM AUTHOR]

Modern methods of designing and constructing hydraulic pumps can no longer be imagined without the use of appropriate mathematical models, phenomena and processes that take place in concrete constructions. A mathematical model of a process represents an analytical interpretation with certain assumptions. Obtaining a mathematical model requires detailed theoretical research, based on knowledge of the laws of fundamental sciences and scientific disciplines, in order to fully understand and interpret the process, upon which assumptions are adopted and model equations are defined. As mobile machines continue to evolve, so do the demands on their hydraulic systems. Efficiency, automation and other major industry trends present ongoing development opportunities for the hydraulic systems used in mobile applications. Hydraulic systems remain an important component in a range of mobile applications, from construction and agricultural equipment to heavy trucks, because of the power density they can provide for a range of work functions. The power density of hydraulics remains unmatched in many applications. The pump, as an essential hydraulic component, converts mechanical energy into hydraulic energy with a relatively small amount and speed of fluid. [ABSTRACT FROM AUTHOR]

The editorial discusses the importance of power transmission and control in various machinery, including vehicles, airplanes, and ships. The Special Issue focuses on new achievements in hydraulic and pneumatic systems, highlighting advancements in design, analysis, and control strategies. The papers cover topics such as hydraulic system design, pneumatic systems, and analytical models for optimization, aiming to enhance the reliability and efficiency of power transmission systems. Emerging technologies like cybernetic physic systems and artificial intelligence are expected to drive further innovation in fluid power transmission and control. [Extracted from the article]

At the present stage, the issues of sustainable development of the Russian agricultural sector and strengthening its technical potential in the context of sanctions pressure are becoming especially relevant. One of the most useful tools in solving these problems is leasing. The purpose of this article is to analyse promising directions for the development of leasing as the effective form of technical support for agricultural production in the current conditions. The study has used general scientific methods (analysis and synthesis, economic and statistical, graphical, etc.). The main trends now emerging in the domestic market are highlighted from the point of view of manufacturers of agricultural machinery and goods. With consideration to the current situation in the domestic market, decreased prices for agricultural products and rapid growth of prices for agricultural machinery, the most promising areas for the use of leasing mechanisms have been proposed to help accelerate technical re-equipment in the agricultural sector: application of leasing solutions in the agricultural industry that have already been tested and proven successful in other market segments (for example, subscription, agrocarsharing, etc.); combination of leasing mechanisms with trade-in programmes which make it possible to comprehensively solve issues not only of updating machinery and tractor fleet, but also of recycling worn-out agricultural equipment; use of leasing mechanisms for the development of machine and technological complexes as a modern form of joint use of machinery in agriculture which allows accelerating technical equipment of the industry. The proposed directions can help ensure systemic development and quicken the technical re-equipment of the Russian agricultural sector.

Introduction: In the context of the deceleration of farmland scale management, a crucial question arises in agricultural development theory and practice: will service driven scale management in the production process become a viable alternative to farmland scale management, serving as the secondary pathway to achieve scale management in China? Methods: To address this issue, an empirical analysis on the impact of agricultural machinery services on farmland transfer was conducted in this article using data from the China Rural Revitalization Survey database comprising 302 villages and 2,871 micro-farmers in 2020 by utilizing the ordinary least squares method and instrumental variable method. Results and discussion: The results show that the implementation of agricultural machinery services will increase the rate of farmland transfer in villages. The effect of agricultural machinery services on increasing the profitability of large-scale farmers surpasses its effect on smallholder farmers, thereby motivating smallholder farmers to transfer farmland to large-scale farmers, consequently fostering the consolidation of farmland. In addition, power-intensive service links are more effective in increasing the rate of farmland transfer and fostering the concentration of farmland transfer compared to control-intensive service links. The impact of agricultural machinery services on enhancing the rate of farmland transfer and fostering the consolidation of farmland transfer is more pronounced on the plain areas compared to the hillmountainous areas. Consequently, this article demonstrates that service-driven scale management and farmland scale management are not merely alternatives but rather complementary strategies that mutually reinforce each other. [ABSTRACT FROM AUTHOR]

Agricultural machinery has been one of the most important sources of air pollution in China. It is very necessary to do more research on its emissions. This study aims to investigate the effects of burning different proportions of biodiesel in agricultural tractors at high altitudes on the emission characteristics of CO (carbon monoxide), HC (hydrocarbon), NOX (nitrogen oxides), and PN (particulate number) under the actual operating conditions. A series of tests were carried out on the blending B10 (10% by volume of soya bean methyl ester biodiesel + 90% by volume of diesel fuel), B20, B30, and diesel fuel (B0) in a National III emission standard agricultural tractor at an altitude of 2 200 m during rotary plowing (with the engine speed at 1 500 to 1 700 r/min and the engine power at 20 to 30 kW). OBD (board diagnostics) and PEMS (portable emission measurement system) were used to collect data on the tractor operating conditions and pollutants, respectively. The results showed that the biodiesel slightly reduced the fuel economy for the brake-specific fuel consumption (BSFC), which increased by 2.31%, 2.71%, and 2.04% for B10, B20, and B30, respectively, compared with B0. The emission factors were also obtained for the CO, HC, NOX, and PN of agricultural tractors using engine power. Specifically, the CO, HC, and PN emission factors of agricultural tractors were reduced by 10.15%, 12.73%, and 32.10% for B10, B20, and B30, respectively, compared with B0 using biodiesel engine power; 13.22%, 26.45% and 30.58% for B0-based power; and 8.22%, 10.91% and 30.58% for B0-based power; and 8.22%, 10.91% and 30.58% for B30-based PN emission. B0 power-based PN emission factors were reduced by 8.22%, 10.91%, and 14.17%, respectively. The CO, HC, and PN emission factors increased with the increase in the percentage of biodiesel. Biodiesel slightly increased the NOX emissions from agricultural tractors. The NOX emission factor increased, as the proportion of biodiesel increased. The NOX emission factors for B10, B20, and B30 increased by 1.60%, 2.78%, and 4.20%, respectively, compared with B0-based engine power. The power-based NOX emission factors of all tested fuels in the plateau environment were more than three times higher than the recommended values of the Guideline. There was a great variation in the pollutant emission of the tractor during actual operation. A tradeoff was made on the fuel economy and the effect of biodiesel on NOX emissions, in order to reduce the pollutant emissions from agricultural tractors during actual operation at high altitudes. Biodiesel can be expected to serve as an alternative fuel in small proportions for agricultural tractors. The CO, HC, and PN emissions can be reduced to avoid the use of fossil fuels. The finding can provide a strong reference to optimize the actual control of the pollutant emission of biodiesel combustion in agricultural tractors under high-altitude environments. The emission database of agricultural machinery can also be supplemented to reduce environmental pollution in high-altitude areas. [ABSTRACT FROM AUTHOR]

To study the influence of speed factors on the stability of a tractor automatic navigation system, combined with the neural network control theory, the authors proposed a dual-objective joint sliding mode control method based on lateral position deviation and heading angle deviation, using a back propagation neural network to establish a two-wheel tractor-path dynamics model and a straight-line path tracking deviation model. The overall system simulation was carried out using Matlab/Simulink, and the reliability of the control method was verified. The experimental results showed that when the tractor was tracked with the automatic control of a linear path under the condition of variable speed, the maximum deviation of the lateral position deviation was 12.7 cm, and the average absolute deviation was kept within 4.88 cm; the maximum deviation of the heading angle deviation was 5°, and the average absolute deviation was kept within 2°; the maximum value of the actual rotation angle was 3.13°, and the standard deviation of the fluctuation was within 0.84°. Under the conditions of constant speed and variable speed, using the joint sliding mode control method designed by the authors, the dual-objective joint control of lateral position deviation and heading angle deviation could be realized, the controlled overshoot was small, the controlled deviation was small after reaching a stable state, and the adaptability to speed factors was strong, which basically could meet the accuracy requirements of farmland operations. [ABSTRACT FROM AUTHOR]

As it is known, the modern agricultural tractor is no longer just a machine capable to pull agricultural trailers and to operate implements but has evolved into a multi-purpose and mobile energy source with standardized interfaces (mechanical, hydraulic and electronic) to connect to a several typologies of agricultural operating machines. It follows that the selection of the most appropriate tractors for the specific production realities is a crucial aspect for farmers, advisors, contractors and farm machinery experts. The tractors choice thus must consider different parameters, concerning not only the cost of the machines but also their dimensions, power, weight, technological level, etc. The availability of simplified models for estimating the purchase investment and sizing the machine in relation to its mechanical characteristics could be a useful tool in the choice of the tractor more suitable for the specific agricultural context. The aim of this study was to collect and to analyse the technical parameters of tractors present on the Italian market (more than 1300 models), divided into: i) four wheel-drive (4WD) standard tractors, ii) two wheel-drive (2WD) standard tractors, iii) narrow track 4WD tractors, iv) Isodiametric specialized 4WD tractors, v) crawler tractors and vi) rubber-tracked tractors), in order to define the most relevant parameter-to-parameter and parameter-to-price relations for updating reference models to calculate the machine price and the weight to engine power ratio. Other relations, including the three-point hitch efficiency with respect to tractor's weight and the relationship between the rated engine power and its displacement, are proposed in order to provide synthetic tools to characterise and to compare - from the mechanical point of view - the different categories of agricultural tractors. [ABSTRACT FROM AUTHOR]

Autonomous driving technology for agricultural machinery can maximise crop yield, reduce labour costs, and alleviate labour intensity. In response to the current low degree of automation and low tracking accuracy of driving paths in agricultural equipment, this research proposes an unmanned agricultural machinery operating system based on an improved fuzzy adaptive PD control algorithm. Firstly, mechanical kinematic models and fuzzy adaptive control algorithms are introduced to achieve autonomous driving, and parameter settings and speed adjustments are made while considering errors. Secondly, in the autonomous driving operation system, taking a certain rice machine as an example, perception information, trajectory design, dynamic control, operation supervision, and remote control design are carried out. The experimental results show that the improved fuzzy algorithm exhibits smaller deviation results in driving path tracking, with an average error between the actual path and the expected path of less than 0.001 m. In different testing scenarios, compared with the actual control results, the maximum deviation of the control system platform in straight sections is less than 2.8 m, which is more stable. More than 95% of the lateral deviation results in the road sections are within 0.11 m. And the tracking distance error of the proposed method in the straight and curved segments is relatively small, far smaller than other comparative algorithms. The unmanned agricultural machinery operation system proposed in this study can significantly improve the efficiency and accuracy of agricultural machinery work, promote the development of intelligent and modern agricultural machinery, and provide reference value and important contributions to social and economic development as well as the progress and promotion of related technologies. [ABSTRACT FROM AUTHOR]

The article focuses on the utilization of farm machinery through Custom Hiring Centers (CHCs) from a farmer's perspective, with a particular emphasis on the post-pandemic scenario in Vijayapur district, Karnataka, India. It explores the role of CHCs in improving farm mechanization, addressing labor shortages, and enhancing productivity by providing affordable access to agricultural machinery for small and marginal farmers.

To rapidly dry peanut pods and effectively address mold issues resulting from rainy weather, this article provides an analysis of the current research status of global peanut digging, inverting, and harvesting technologies. Based on a two-stage peanut harvesting model, the operating principles of a high-quality peanut digging-inverter are elaborated upon, and the design of key machine components is discussed. The evaluation metrics for the operation of a high-quality peanut digging-inverter include the rate of vine inverting, the soil content rate, the rate of fallen pods, and the rate of buried pods. Utilizing theoretical analysis, the Box–Behnken experimental design is employed to investigate the operating parameters of these machines, with the tractor's running speed, the linear speed of the inverting roller, and the linear speed of the conveyor chain serving as the experimental factors in a three-factor, three-level experimental study. A regression model is established to analyze the impact of each factor on operational quality and to comprehensively optimize the influencing factors. The experimental results indicate that all factors significantly affect the rate of vine inverting. The tractor's running speed (X1) and the linear speed of the inverting roller (X3) significantly influence the rate of buried pods, while the linear speed of the conveyor chain (X2) does not have a significant effect on this rate. Similarly, the tractor's running speed (X1) and the linear speed of the inverting roller (X3) significantly affect the rate of fallen pods, whereas the linear speed of the conveyor chain (X2) does not. Furthermore, the linear speed of the conveyor chain (X2) and the linear speed of the inverting roller (X3) significantly impact the soil content rate, while the tractor's running speed (X1) does not. The optimal combination of operating parameters is a tractor running speed of 1 m/s, a conveyor chain linear speed of 1.3 m/s, and an inverting roller linear speed of 2.1 m/s. Under these conditions, the high-quality peanut digging-inverter achieves a vine inverting rate of 89.29%, a buried pod rate of 0.31%, a fallen pod rate of 0.74%, and a soil content rate of 8.11%. The experimental values for each evaluation index exhibit relative errors of 1.14%, 6.45%, 1.35%, and 0.13% compared to the optimized values provided by the Design-Expert software. The findings of this research will facilitate the development of hardware conditions for the future intelligent and information-based harvesting of peanuts, thereby establishing a solid foundation for the high-quality initial processing of oilseeds. [ABSTRACT FROM AUTHOR]

Plastic film mulching technology can effectively enhance crop yield and quality, and the use of mulch has been increasing in recent years; however, the problem of mulch residue is worsening due to the large amount of recycling work and slow natural degradation. In this study, a potato seedling killing and residual film recycling machine is designed to provide good working conditions for potato harvesters before harvesting in response to the problems of difficult separation of film tangles, the low net rate of recycling due to the mixing of residual film with soil, and the high soil content in residual film recycling operations in northwest China. The machine is based on the potato monoculture and double row planting mode in Gansu area. This paper puts forward the overall design scheme and carries out the theoretical analysis and parameter determination of the key components, such as the seedling killing device, the film surface cleaning device, the film unloading device, and so on. Using EDEM software to carry out the virtual simulation test and Design-Expert13 to analyze the test results, we determined the optimal working scheme for the machine, with a forward speed of 0.8 m/s, a film gap of 125 mm, and a spiral stirrer speed of 600 r/min. Based on a field test for verification, the test results show that the machine's residual film recovery rate was 83.3%, the impurity rate was 3.8%, and the rate of injury to the potatoes was 1.4%. The machine meets the requirements of national and industry standards, and it can simultaneously realize straw crushing, film surface cleaning, residual film recycling, and hydraulic film unloading operations, with better operating results and while reaching the expected results. It can also provide a reference for the design and testing of a seeding and residual film recycling machine. [ABSTRACT FROM AUTHOR]

Field-road trajectory segmentation, which aims to automatically divide a trajectory into a sequence of field-road segments, is one of the important tasks of the agricultural machinery trajectory process. This study aims to enhance the performance of the field-road segmentation problem by introducing a novel parameter optimization strategy that relies on metaheuristic algorithms. The utilization of the metaheuristic algorithm offers precise and efficient techniques for determining parameter combinations in the field-road segmentation model. A novel enhanced optimization algorithm called the chaos sensing slime mould algorithm (CSSMA), aimed at determining out the optimal parameter combination from a large constrained solution space is proposed to further improve the segmentation performance of the model. First, to strengthen the exploration capability of the algorithm, a chaotic strategy is used to initialize the search agents. Furthermore, the CSSMA incorporates the beetle antennae search algorithm and the genetic algorithm to improve its exploitation potential. Ultimately, a proposed technique for updating control parameters in a nonlinear and dynamic manner aims to achieve a balance between exploration and exploitation. This strategy involves adaptively adjusting computing operations based on feedback from the optimization process. In order to assess the effectiveness of the CSSMA, the algorithm is compared to other metaheuristic algorithms using 23 standard benchmark functions. Furthermore, the CSSMA is utilized to address the practical implementation of the parameter optimization approach for field-road segmentation. The goal is to achieve the utmost accuracy in segmentation and compare it with the current method. Experimental results demonstrate the superior convergence and speed of CSSMA, along with its benefits in extracting optimal parameter structures. It surpasses prior techniques in handling the complexities of field-road trajectory processing problems including several models and nonlinearity features. Additionally, it offers a comprehensive solution for optimizing parameters in various field-road segmentation models. [ABSTRACT FROM AUTHOR]