Agricultural Machinery Operation Research Articles

Model and Parameter Adaptive MPC Path Tracking Control Study of Rear-Wheel-Steering Agricultural Machinery

To further enhance the precision and the adaptability of path tracking control, and considering that most of the research is focused on front-wheel steering, an adaptive parametric model predictive control (MPC) was proposed for rear-wheel-steering agricultural machinery. Firstly, the kinematic and dynamic models of rear-wheel-steering agricultural machinery were established. Secondly, the influence laws of curvature and velocity on the prediction horizon Np, control horizon Nc, and preview value Npre were obtained by simulating and analyzing the factors influencing the MPC tracking effect. The results revealed that raising Npre can improve curve tracking performance. Np was correlated negatively with the curvature change, whereas Nc and Npre were positively connected. Np, Nc, and Npre were correlated positively with the velocity change. Then, the parameters for self-adaptation of Np, Nc, and Npre were accomplished via fuzzy control (FC), and particle swarm optimization (PSO) was utilized to optimize the three parameters to determine the optimal parameter combination. Finally, simulation and comparative analysis were conducted to assess the tracking effects of the manual tuning MPC, the FC_MPC, and the PSO_MPC under U-shaped and complex curve paths. The results indicated that there was no significant difference and all three methods achieved better tracking effects under no disturbance, with the mean absolute value of lateral error ≤0.18 cm, standard deviation ≤0.37 cm, maximum deviation of U-shaped path <2.38 cm, and maximum deviation of complex curve path <3.15 cm. The mean absolute value of heading error was ≤0.0096 rad, the standard deviation was ≤0.0091 rad, and the maximum deviation was <0.0325 rad, indicating that manual tuning can find optimal parameters, but with high uncertainty and low efficiency. However, FC_MPC and PSO_MPC have better adaptability and tracking performance compared to the manual tuning MPC with fixed horizons under variable-speed disturbance and are more able to meet the actual needs of agricultural machinery operations.

Design and Test of Intelligent Farm Machinery Operation Control Platform for Unmanned Farms

The unmanned farm control platform is of great significance in promoting the supervision of farm production with less manpower or autonomous operation of farm machinery and the construction of farm informatization. Addressing the existing control platform for farm location information acquisition is time-consuming, labor-intensive, and lacks the whole process control of multiple types of farm machinery. In this paper, we propose an Internet of Things (IoT) control scheme for intelligent farm machinery operation of unmanned farms and design the access standards for multiple types of farm machinery, as well as realize the remote control of intelligent farm machinery operation by constructing a remote control model. A high-precision map construction method is designed to improve the DeepLabV3+ algorithm to identify fields and roads. The control models of path planning, remote task, remote control, and safety system are built to achieve the remote control of intelligent agricultural machinery operation. The proposed technology is implemented in the platform integration and application tests are carried out. The error of the constructed high-precision map is less than 3 cm, the completeness rate of the automatic boundary extraction rate is 96.71%, and the correctness rate is 95.63%, which can be used to obtain the boundary instead of manual labeling or on-site point picking. The use of the platform for the simultaneous control of three farm machinery operations reduces the number of people in operation and production and reduces the professional requirements of the personnel, which will promote the management of the entire farm by one person or even by no one in the future.

Task Allocation of Multi-Machine Collaborative Operation for Agricultural Machinery Based on the Improved Fireworks Algorithm

Currently, the multi-machine collaboration of agricultural machinery is one of the international frontiers and a topic of research interest in the field of agricultural equipment. However, the multi-machine cooperative operation of agricultural machinery is mostly limited to the research on task goal planning and cooperative path optimization of a single operation. To address the mentioned shortcomings, this study addresses the problem of multi-machine cooperative operation of fertilizer applicators in fields with different fertility and fertilizer cooperative distribution of fertilizer trucks. The research uses the task allocation method of a multi-machine cooperative operation of applying fertilizer-transporting fertilizer. First, the problems of fertilizer applicator operation and fertilizer truck fertilizer distribution are defined, and the operating time and the distribution distance are used as optimization objectives to construct functions to establish task allocation mathematical models. Second, a Chaos–Cauchy Fireworks Algorithm (CCFWA), which includes a discretized decoding method, a population initialization with a chaotic map, and a Cauchy mutation operation, is developed. Finally, the proposed algorithm is verified by tests in an actual scenario of fertilizer being applied in the test area of Jimo District, Qingdao City, Shandong Province. The results show that compared to the Fireworks Algorithm, Genetic Algorithm, and Particle Swarm Optimization, the proposed CCFWA can address the problem of falling into a local optimum while guaranteeing the convergence speed. Also, the variance of the CCFWA is reduced by more than 48% compared with the other three algorithms. The proposed method can realize multi-machine cooperative operation and precise distribution of seeds and fertilizers for multiple seeding-fertilizer applicators and fertilizer trucks.

The Evolution and Potential of Precision Agriculture in China Anchored In "3S" Technology.

Agriculture serves as a linchpin for the national economy. With the accelerated evolution of information technology and emergent high-tech innovations, the digitization of agriculture has emerged as a pivotal trajectory for contemporary agricultural advancement. Conventional practices encompassing planting, fertilization, and harvesting are transitioning towards more quantitative, localized, and technologically-driven precision agriculture. Concurrently, with the global surge in "precision agriculture", Chinese academicians have progressively delved into its study. Over the past three decades, the "3S" technology-centric "precision agriculture" in China has witnessed commendable advancements, exemplified by innovations such as the Global Agricultural Remote Sensing Quick Report System and the Beidou Satellite Navigation System-based autonomic agricultural machinery operation. However, the modernization of Chinese agriculture exhibits certain limitations, including limited adoption, suboptimal integration, and a scarcity of high-caliber experts. Given the swift progression of Chinese scientific and economic domains, the "3S" technology is poised to surmount these challenges, enhancing the modernization of Chinese agriculture. Hence, "precision agriculture" anchored in "3S" technology retains significant growth prospects within China.

Repair and adjustment manual for mowers

The manual for repair and adjustment of mowers is intended for farmers, machine operators and specialists involved in technical service and operation of agricultural machinery on farms and at repair and maintenance enterprises. When developing the manual, documentation from manufacturers, materials from research centers, and best practices in mower repair were used. The manual contains the main malfunctions of mower components and assemblies, provides instructions for eliminating them, and provides recommendations for cleaning, adjustment, running-in, storage and technological adjustment of the main working parts.

Monitoring of farmland dust during tillage and sowing operations and comparative analysis of cultivation modes in the suburban areas of Beijing

Agricultural machinery's tillage and sowing processes can generate much farmland dust, adversely affecting the surrounding environment. Clarifying the dust emissions from various agricultural machinery operations and selecting a reasonable cultivation mode is significant for reducing dust in the atmosphere and protecting the environment. However, a systematic exploration of the agricultural dust generated during agricultural machinery operations is needed. Here, we designed a dust monitoring system to monitor farmland dust (PM2.5, PM10, and TSP) and environmental parameters at different points. The system is applied to monitor the dust emission concentration caused by agricultural machinery. We found the concentration of PM2.5, PM10, and TSP from straw returning operations to be 2.06, 3.16, and 2.84 times higher than that of ploughing operations. The increase in cultivation speed will lead to an increase in dust concentration. In straw returning operations, compared with low-speed, the increase in PM2.5, PM10, and TSP concentration during medium-speed cultivation is 21.6%, 12.5%, and 16.8%, whereas high-speed cultivation is 68.2%, 38.9%, and 35.7%, respectively. Moreover, conservation tillage modes can significantly suppress dust emissions. We found that conservation tillage produced the lowest concentrations of PM2.5, PM10, and TSP during the tillage and sowing stages, which were 64.7%, 73.1%, and 72.1%, compared to traditional tillage. As the amount of straw coverage increases, the dust concentration decreases. The average PM2.5, PM10, and TSP concentration increment of NTS with LSMQ increased by 307.8%, 344.8%, and 346.8%. Our results demonstrate that adopting a conservation tillage and sowing model and increasing straw coverage can reduce dust emissions. The research results have specific guiding significance for reducing dust emissions during the spring and autumn cultivation. We anticipate evaluating further the contribution of small particulate matter generated during agricultural machinery operations to air pollution, and our research will provide a basis for future work.

Adoption mode of agricultural machinery and food productivity: evidence from China

Many researchers argue that the adoption of agricultural mechanization services (AMSs) is an important way for smallholder farmers in China to engage in modern agricultural production. However, the impact of the adoption mode of agricultural machinery on food productivity remains under-analyzed. We investigate the links between the adoption mode of agricultural machinery and food productivity using data on 795 grain farmers collected from the North China Plain. The results indicate that, compared with service outsourcing (SO), self-purchase (SP) improves the technical efficiency of farms; however, it reduces the input efficiency of agricultural machinery. The channel of the effect is that, although SO can reduce the redundancy of agricultural machinery's input, the opportunistic behavior of AMS suppliers and labor supervision problems lead to a decline in agricultural machinery's operation quality. The impact of the adoption mode of agricultural machinery on food productivity is asymmetrical among different types of farmers. Large-scale and professional farmers benefit more from SP, whereas small-scale and part-time farmers benefit more from SO. The AMS is not perfect, and the Chinese government should pay close attention to the loss of technical efficiency in agricultural production caused by the opportunistic behavior of AMS suppliers. Therefore, it is necessary to adjust the agricultural machinery subsidy policy and reduce the transaction cost of AMS.

ПРИМЕНЕНИЕ ВТОРИЧНЫХ ПОРОШКОВЫХ МАТЕРИАЛОВ ДЛЯ ВОССТАНОВЛЕНИЯ БРОНЗОВЫХ ДЕТАЛЕЙ ГИДРОМАШИН

During the operation of agricultural machinery, machine parts and their connections lose their operability, which requires their replacement. The cost of restoration is 50-60 percent of the cost of a new part. The presence of non-ferrous metal and alloy parts in the equipment, and in particular bronze, makes the issue of restoration even more urgent due to the high cost of such parts. Methods using powder materials are particularly successful in restoring worn parts. It is characteristic that in order to reduce the cost of such technologies, it is possible to resort to recycling of metal waste and obtain powder by electroerosive dispersion without special economic costs. (Research purpose) The research purpose is studying the restoration of bronze parts of hydraulic machines with secondary powder materials and analyzing the tribotechnical indicators of the coatings obtained. (Materials and methods) The following equipment was used in the work: an Elitron-52B pulse generator, a scanning electron microscope, a spark plasma sintering unit, an electric spark alloying unit, an X-ray fluorescence spectrometer, a metallographic microscope, a tribometer, analytical scales, a drying cabinet, a set of sieves. (Results and discussion) Electrodes were made from bronze powder obtained by the method of electroerosive dispersion. Using electric spark alloying, samples with coatings from a standard and a new obtained electrode were created. The tribotechnical properties of these coatings were compared. Metallographic and chemical analyses were performed. (Conclusions) The coatings obtained with the new electrode showed the best tribotechnical characteristics in dry friction. At boundary friction, their performance was worse, which may be due to an incorrect choice of lubrication, modes of electric spark alloying and a violation of the technology for obtaining the electrode itself. In general, the use of secondary powder materials for the restoration of bronze parts is relevant.

Social and hygienic aspects of preserving the labor potential of the Russian Federation grain complex sustainable development

An analysis of the social and hygienic aspects of preserving labor potential as an important component of the sustainable development of the Russian grain complex was carried out. Based on social and hygienic studies, an assessment of the factors of maintaining labor potential during the operation of agricultural machinery is given. It has been established that technical and ergonomic imperfections, moral and physical aging of agricultural machinery create harmful working conditions, the chronic impact of which leads to the development of occupational diseases among agricultural machine operators (vibration disease, radiculopathy of the lumbosacral level, sensorineural hearing loss), as well as the development and severity of the course of general diseases (arterial hypertension, respiratory diseases). The interviewed machine operators (95%) associated the deterioration of their health with the negative impact of production factors (r=0.9, p<0.005) and social and living conditions that affect the prestige of agricultural work and the formation of personnel shortages. Technical re-equipment, development of rural infrastructure, ensuring safe working conditions, maintaining health, increasing the social well-being of workers are the main resources for the sustainable development of the agroindustrial complex of the Russian economy.

Path Planning and Tracking Control of Tracked Agricultural Machinery Based on Improved A* and Fuzzy Control

In order to improve the efficiency of agricultural machinery operations and reduce production costs, this article proposes a path planning algorithm based on the improved A* algorithm (IA*) and a tracking controller based on fuzzy sliding mode variable structure control (F-SMC) to meet the operation requirements of tracked agricultural machinery. Firstly, we introduce a heuristic function with variable weights, a penalty, and a fifth-order Bezier curve to make the generated path smoother. On this basis, the ant colony algorithm is introduced to further optimize the obtained path. Subsequently, based on fuzzy control theory and sliding mode variable structure control theory, we established a kinematic model for tracked agricultural machinery as the control object, designed a fuzzy sliding mode approaching law, and preprocessed it to reduce the time required for sliding mode control to reach the chosen stage. The simulation experiment of path planning shows that compared with A*, the average reduction rate of the path length for IA* is 5.51%, and the average reduction rate of the number of turning points is 39.01%. The path tracking simulation experiment shows that when the driving speed is set to 0.2 m/s, the adjustment time of the F-SMC controller is reduced by 0.99 s and 1.42 s compared to the FUZZY controller and PID controller, respectively. The variance analysis of the adjustment angle shows that the minimum variance of the F-SMC controller is 0.086, and the error converges to 0, proving that the vehicle trajectory is smoother and ultimately achieves path tracking. The field test results indicate that the path generated by the IA* algorithm can be tracked by the F-SMC controller in the actual environment. Compared to the A* algorithm and FUZZY controller, the path tracking time reduction rate of IA* and F-SMC is 29.34%, and the fuel consumption rate is reduced by 2.75%. This study is aimed at providing a feasible approach for improving the efficiency of tracked agricultural machinery operations, reducing emissions and operating costs.

ТЕХНОЛОГИЯ БЕЗРАЗБОРНОГО ВОССТАНОВЛЕНИЯ И ПРОДЛЕНИЯ РЕСУРСА ДЕТАЛЕЙ И МЕХАНИЗМОВ СИЛОВЫХ УСТАНОВОК СЕЛЬСКОХОЗЯЙСТВЕННОЙ ТЕХНИКИ

The technology for in-place restoration and extension of the life of worn metal connections of parts and mechanisms of power plants of agricultural machinery, as well as means for its implementation, have been proposed. The analysis of the operation of agricultural machinery showed that its operation is carried out mainly in field conditions and with significant loads on the power plant, while the thermal stress of the crank mechanism parts increases. To solve this problem, a special lubricating additive has been developed, which is added to the engine oil through the oil filler neck. The technology initially includes a comprehensive assessment of the technical condition of the power plant of each model of equipment based on the operating oil parameters, the development of a method for in-place restoration of worn metal connections of parts and mechanisms of the power plant based on the use of a developed anti-friction lubricant additive added to the operating engine oil. The express analysis module is designed to determine the parameters of operating motor oil. The introduction of the developed technology for in-place restoration and extension of the life of worn metal connections of parts and mechanisms of power plants of agricultural machinery will lead to a reduction in fuel consumption by up to 7%, an increase in the life of worn-out diesel units, and will increase the readiness of individual types of agricultural machinery and the overall technical readiness of the equipment fleet.

Multi-Crop Navigation Line Extraction Based on Improved YOLO-v8 and Threshold-DBSCAN under Complex Agricultural Environments

Field crops are usually planted in rows, and accurate identification and extraction of crop row centerline is the key to realize autonomous navigation and safe operation of agricultural machinery. However, the diversity of crop species and morphology, as well as field noise such as weeds and light, often lead to poor crop detection in complex farming environments. In addition, the curvature of crop rows also poses a challenge to the safety of farm machinery during travel. In this study, a combined multi-crop row centerline extraction algorithm is proposed based on improved YOLOv8 (You Only Look Once-v8) model, threshold DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering, least squares method, and B-spline curves. For the detection of multiple crops, a DCGA-YOLOv8 model is developed by introducing deformable convolution and global attention mechanism (GAM) on the original YOLOv8 model. The introduction of deformable convolution can obtain more fine-grained spatial information and adapt to crops of different sizes and shapes, while the combination of GAM can pay more attention to the important feature areas of crops. The experimental results shown that the F1-score and mAP value of the DCGA-YOLOv8 model for Cabbage, Kohlrabi, and Rice are 96.4%, 97.1%, 95.9% and 98.9%, 99.2%, 99.1%, respectively, which has good generalization and robustness. A threshold-DBSCAN algorithm was proposed to implement clustering for each row of crops. The correct clustering rate for Cabbage, Kohlrabi and Rice reaches 98.9%, 97.9%, and 100%, respectively. And LSM and cubic B-spline curve methods were applied to fit straight and curved crop rows, respectively. In addition, this study constructed a risk optimization function for the wheel model to further improve the safety of agricultural machines operating between crop rows. This indicates that the proposed method can effectively realize the accurate recognition and extraction of navigation lines of different crops in complex farmland environment, and improve the safety and stability of visual navigation and field operation of agricultural machines.

Image‐based crop row detection utilizing the Hough transform and DBSCAN clustering analysis

AbstractMore accurate methods for crop row detection benefit intelligent operation of agricultural machinery, especially avoiding mishandling or crushing crops. For achieving such a target, a traditional method combining the ExGR exponents, Otsu algorithm, Canny method, Hough transform and DBSCAN clustering analysis is proposed so that centerlines of crop rows can be detected effectively without manual intervention. Specifically, ExGR exponents are first adopted to gray green plants. The threshold of binarization will be further obtained by the Otsu algorithm. Further adopting the edge detection algorithm (Canny), edges of crops can be determined. Finally, combining the Hough transform and DBSCAN clustering analysis, the crop row detection is effectively available. Utilizing these methods, numerical simulation and their comparisons with existing methods are also achieved. For example, the Canny algorithm is relatively accurate than the Suzuki algorithm as well as their combinations with a geometric center extraction method if the density of weed is high. Compared with the K‐means clustering method, the DBSCAN algorithm is more suitable to characterize crop rows optimally in more complex conditions. It is validated from experiments that the combination of Canny algorithm, Hough transform and DBSCAN clustering is better than other mentioned traditional methods.

A New Remote Sensing Service Mode for Agricultural Production and Management Based on Satellite–Air–Ground Spatiotemporal Monitoring

Remote sensing, the Internet, the Internet of Things (IoT), artificial intelligence, and other technologies have become the core elements of modern agriculture and smart farming. Agricultural production and management modes guided by data and services have become a cutting-edge carrier of agricultural information monitoring, which promotes the transformation of the intelligent computing of remote sensing big data and agricultural intensive management from theory to practical applications. In this paper, the main research objective is to construct a new high-frequency agricultural production monitoring and intensive sharing service and management mode, based on the three dimensions of space, time, and attributes, that includes crop recognition, growth monitoring, yield estimation, crop disease or pest monitoring, variable-rate prescription, agricultural machinery operation, and other automatic agricultural intelligent computing applications. The platforms supported by this mode include a data management and agricultural information production subsystem, an agricultural monitoring and macro-management subsystem (province and county scales), and two mobile terminal applications (APPs). Taking Shandong as the study area of the application case, the technical framework of the system and its mobile terminals were systematically elaborated at the province and county levels, which represented macro-management and precise control of agricultural production, respectively. The automatic intelligent computing mode of satellite–air–ground spatiotemporal collaboration that we proposed fully couples data obtained from satellites, unmanned aerial vehicles (UAVs), and IoT technologies, which can provide the accurate and timely monitoring of agricultural conditions and real-time guidance for agricultural machinery scheduling throughout the entire process of agricultural cultivation, planting, management, and harvest; the area accuracy of all obtained agricultural information products is above 90%. This paper demonstrates the necessity of customizable product and service research in agricultural intelligent computing, and the proposed practical mode can provide support for governments to participate in agricultural macro-management and decision making, which is of great significance for smart farming development and food security.

A high-resolution energy use efficiency assessment of China’s staple food crop production and associated improvement potential

Improving production efficiency of existing agricultural systems is key to promoting sustainable agricultural production. Energy use efficiency in crop production is a key and widely applied index used to measure agricultural production efficiency. Understanding the current status of energy use efficiency in crop production at high spatial resolutions is requisite for developing energy-saving and efficient crop production optimization strategies. This study provides a first attempt at systematically evaluating the county-level energy use efficiency status of China’s three staple food crops (i.e., wheat, maize, and rice) and their associated improvement potential based on the Energy Return on Investment (EROI) method. Results show that average EROI values of wheat, maize, and rice production (i.e., 0.94, 1.1, and 1.3, respectively) substantiate low energy use efficiency under a high improvement potential. Moreover, the energy use efficiency of staple food crop production exhibits considerable spatial heterogeneity. The top 25% counties with the highest EROI values are mainly concentrated in wheat-producing areas of the North China Plain, maize-producing areas of northeastern China and Inner Mongolia, and rice-producing areas of the central Yangtze River region. Crop yields and agricultural machinery inputs are the main EROI driving factors. Furthermore, scenario analysis shows that regional EROI gaps in wheat, maize, and rice production could be narrowed by increasing yields and improving agricultural machinery operation efficiency. Overall, this study provides an improved understanding of the current energy use efficiency status of staple food crop production in China. Recommended solutions are also proposed to improve the EROI of staple food crops. This provides scientific support in formulating regional optimal management strategies for crop production and guidance for farmers to conduct more efficient production practices. Additionally, our framework can also be adopted and built upon in regions throughout the world that experience similar low energy use efficiency conditions in agricultural production.

Path Planning and Control System Design of an Unmanned Weeding Robot

Aiming at the demand by unmanned farms for unmanned operation in the entire process of field management, an unmanned plant protection robot for field management was developed based on a platform comprising a traditional high-clearance spray rod sprayer, integrated unmanned driving technology, image recognition technology, intelligent control technology, and precision operation technology. According to the agricultural machinery operation mode, agricultural machinery path planning, linear path tracking, and header path tracking algorithms were developed. Based on the overall structure and working principle of the chassis, the robot control system, steering control system, and operation control system were set. Based on the YOLOv5 image recognition algorithm, the crop–weed recognition model was developed. After 6000 rounds of training, the accuracy, recall, and mean average precision of the model were 87.7%, 84.5%, and 79.3%, respectively. Finally, a field experiment was carried out with the unmanned plant protection robot equipped with a complete system. Results show that the average lateral error of the robot is 0.036 m, the maximum lateral error is 0.2 m, the average root mean square error is 0.053 m, the average velocity error is 0.034 m/s, and the average root mean square error of velocity is 0.045 m/s when the robot works in a straight line. In weeding operations, the area ratio of weedy zones to field is 25%, which saves 75% of the herbicide compared to that dispensed in full spraying mode. The unmanned plant protection robot designed in this study effectively achieves machinery’s autonomous operation, providing valuable insights for research in unmanned farming and autonomous agricultural machinery.

Discrete element contact model and parameter calibration for clayey soil particles in the Southwest hill and mountain region

Distinct physical properties of red clay soil in hilly and mountainous regions of southwest China, including high adhesiveness and density, challenge the operation of agricultural machinery. A scarcity of accurate discrete element simulation parameters for this soil type restricts computational modeling. The study was focused on red clay soil with a moisture content of 12.50% ± 1% and a measured repose angle of 35.54°. The soil's inherent physical properties were identified through experimental assessments. Soil contact mechanical parameters were obtained from the GEMM database, and optimal contact parameter ranges were determined using Steepest Ascent Experiments, with the simulated soil particle repose angle serving as the response value. A second-order regression model was developed using a quadratic regression rotation orthogonal combination test. By taking the actual repose angle as the optimization criterion, parameters were optimized. The optimal contact mechanical parameters in EDEM simulations were identified as: JKR surface energy at 8.981 J/m2, recovery coefficient at 0.474, dynamic friction coefficient at 0.196, and static friction coefficient at 0.45. The model yielded a repose angle of 36.21°, closely corresponding with the observed value, with a relative error of 1.80%. The parameters calibrated in this study offer a valuable reference for future soil-tool interaction studies and tillage implement optimization in these regions.

The role of agricultural machinery in improving green grain productivity in China: Towards trans-regional operation and low-carbon practices

Agricultural machinery is effective in improving food production and labor productivity, but it also raises environmental concerns. The impacts of agricultural machinery on the green total factor productivity (GTFP) of grain in China are still under debate in the scientific literature. This study proposed an integrated framework for confronting this issue. The findings suggest that both agricultural mechanization and the GTFP of grain demonstrate a consistent upward trend with moderate fluctuations between 2001 and 2019. By expanding the spatial pattern, there is a positive spatial correlation between them. In addition, we compared the results in three grain functional areas by using the spatial Durbin model (SDM). There were significantly positive spatial spillover effects in major grain-producing areas, which were attributed to the trans-regional operation of agricultural machinery and its carbon reduction effects on neighboring provinces. Notably, the direct effects in major grain-marketing areas and producing-marketing balance areas were significantly positive because agricultural machinery has played a critical role in filling the gap in local labor shortages in grain production. Accordingly, adaptive strategies including building the “Internet + agricultural machinery operation” platform, implementing the land consolidation suitable for machinery, and developing low-carbon agricultural machinery should be fully considered by Chinese policy-makers to promote mechanized agriculture and a low-carbon economy. The findings of this study can help us better understand the role of agricultural machinery in improving green grain productivity in China and thus have significance for the modern and green transformation of agricultural production systems.

Collaborative path planning and task allocation for multiple agricultural machines

Path planning and task allocation are critical concerns in multi-machine collaborative operations for unmanned farms. Nevertheless, several problems remain in the operation of agricultural machinery, such as the slow path planning algorithm, the omission of the working area, and the unreasonable scheduling of machines, resulting in low efficiency and wasted resources. Collaborative and complete coverage path planning was achieved to solve the problems of slow path planning algorithms and the omission of working areas. The farm’s electronic map was constructed using the topological map method. The improved Dijkstra algorithm based on priority queues was combined with three different complete coverage methods: the nested method, the reciprocating method, and the combination of nested and internal spiral path methods. The simulation results show that the improved Dijkstra method based on priority queues can effectively minimize the running time of the algorithm. The reciprocating method has a higher coverage index than the other two methods, with an average coverage rate of 94.73 %. To solve the problem of illogical scheduling of the same type of agricultural machines, an improved ant colony method was presented based on the whole working path to minimize the path cost. The simulation results show that the proposed method can allocate the task properly, and the path cost is reduced by 14 %–33 %. By combining the proposed path planning and task allocation methods, the whole-process path planning of a single agricultural machine and multiple agricultural machines of the same type was achieved, providing a technical solution for promoting the construction of unmanned farms.

The “Tractors and Agricultural Machinery” journal is included in Scopus data base

On July 12, 2023, a festal occasion happened, as our journal, Tractors and Agricultural Machinery, has been accepted for listing in the Scopus international scientometric database.
 Being included in the Scopus is a result of multiyear work of the journals staff on increasing the quality of published materials, attracting authors with high citation index, broadening the journal audience. We highly appreciate the trust of authors and readers who mainly define the journals success. Including in Scopus gives new opportunities for development of the Tractors and Agricultural Machinery as leading peer-reviewed journal of tractor and agricultural machinery industry and RD.
 Tractors and Agricultural Machinery is the oldest scientific and technical journal in its field. It was founded in 1930 as print media of Ministry of Tractor and Agricultural Engineering of the USSR. Today it continues to cover relevant issues of development, manufacturing and operation of tractors, combine harvesters and other agricultural machinery in its 90th volume.