Intelligent Agricultural Machinery Research Articles

Multi-constraint improved RS path planning method for unmanned rice direct seeding machine

Path planning is one of the key technologies that determines the efficiency and quality of field operations using autonomous agricultural machinery. To date, there has been extensive research on global coverage path planning for unmanned agricultural machinery within the working area; however, the issue of “encircling and edging” left by the machinery during headland turns is often overlooked. This study focuses on the operational conditions of unmanned agricultural machinery in the rice fields of southern China. The path planning problem is abstracted under physical constraints, such as the space at the field headland, the agricultural environment (e.g., types of field ridges), the turning characteristics of the machinery, turning radius, working width, and minimum safety distance, into mathematical constraints. This yields an optimized path planning model and sequence combination. Based on the improved Reeds–Shepp curve, an en-circling and edging path planning algorithm is designed. The planned path for the unmanned agricultural machinery from start to finish consists only of arcs and straight lines, suitable for the right-angle turning, reversing, and 180° U-turn operations required for rice field machinery, thus significantly improving the coverage of field edges and missed areas at the headland. A rice direct-seeding machine was used as a test subject for encircling and edging field trials, with tests conducted in rectangular, trapezoidal, and irregular quadrilateral rice fields. The results show that, for rectangular plots, the full-field coverage rate of rice direct-seeding operations can reach 96.44%; for trapezoidal plots, the coverage rate is 95.47%; and for irregular quadrilateral fields, the coverage rate is 95.69%. These results are promising, indicating that the encircling and edging path planning based on the improved Reeds–Shepp curve can effectively increase the full-field coverage rate of unmanned agricultural machinery, improving work quality and meeting the needs of modernized, intelligent agricultural machinery operations.

Maize Kernel Quality Detection Based on Improved Lightweight YOLOv7

As an important cereal crop, maize is a versatile and multi-purpose crop, primarily used as a feed globally, but also is important as a food crop, and has other uses such as oil and industrial raw materials. Quality detection is an indispensable part of functional and usage classification, avoiding significant waste as well as increasing the added value of the product. The research on algorithms for real-time, accurate, and non-destructive identification and localization of corn kernels based on quality classification and equipped with non-destructive algorithms suitable for embedding in intelligent agricultural machinery systems is a key step in improving the effective utilization rate of maize kernels. The difference in maize kernel quality leads to significant differences in price and economic benefits. This algorithm reduced unnecessary waste caused by the low efficiency and accuracy of manual and mechanical detection. Image datasets of four kinds of maize kernel quality were established and each image contains a total of about 20 kernels of different quality randomly distributed. Based on the self-built dataset, the YOLOv7-tiny, as the backbone network, was used to design a maize kernel detection and recognition model named “YOLOv7-MEF”. Firstly, the backbone feature layer of the algorithm was replaced by MobileNetV3 as the feature extraction backbone network. Secondly, ESE-Net was used to enhance feature extraction and obtain better generalization performance. Finally, the loss function was optimized and replaced with the Focal-EOIU loss function. The experiment showed that the improved algorithm achieved an accuracy of 98.94%, a recall of 96.42%, and a Frame Per Second (FPS) of 76.92 with a model size of 9.1 M. This algorithm greatly reduced the size of the model while ensuring high detection accuracy and has good real-time performance. It was suitable for deploying embedded track detection systems in agricultural machinery equipment, providing a powerful theoretical research method for efficient detection of corn kernel quality.

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.

Multi-Vehicle Unmanned Following Operation Driving System Based on OpenMV

In response to the low level of agricultural production automation and labor shortage, to improve the efficiency of agricultural machinery utilization and the reliability of automatic navigation, this paper designs an intelligent agricultural machinery system based on OpenMV for multi-vehicle unmanned following operations. Firstly, the kinematics of the agricultural machinery vehicle is analyzed, the path recognition and location line extraction are carried out using the OpenMV vision module, and the agricultural machinery steering control system is designed using the cascade PID closed-loop control technology, which achieves accurate steering control and path tracking on straight lines and curves. Subsequently, using embedded microcontrollers to manage the workflow, utilizing OpenMV visual modules to receive, store, and process positioning information, and sending control instructions to execution units such as the TB6612FNG motor drive module. Moreover, in response to the communication issues involving multiple HC-05 Bluetooth modules in the system's multi-vehicle communication, corresponding protocol specifications have been developed, and combined with the use of the HC-SR04 ultrasonic module to maintain dual vehicle distance and obstacle detection, achieving multi-vehicle collaborative following operations. The experimental results show that the system achieves a path recognition navigation accuracy of 93.4% and a communication success rate of 97.5% in simulating the process of farm harvesting and transportation, effectively proving the multi-vehicle collaboration of the system in the paper

Headland Identification and Ranging Method for Autonomous Agricultural Machines

Headland boundary identification and ranging are the key supporting technologies for the automatic driving of intelligent agricultural machinery, and they are also the basis for controlling operational behaviors such as autonomous turning and machine lifting. The complex, unstructured environments of farmland headlands render traditional image feature extraction methods less accurate and adaptable. This study utilizes deep learning and binocular vision technologies to develop a headland boundary identification and ranging system built upon the existing automatic guided tractor test platform. A headland image annotation dataset was constructed, and the MobileNetV3 network, notable for its compact model structure, was employed to achieve binary classification recognition of farmland and headland images. An improved MV3-DeeplabV3+ image segmentation network model, leveraging an attention mechanism, was constructed, achieving a high mean intersection over union (MIoU) value of 92.08% and enabling fast and accurate detection of headland boundaries. Following the detection of headland boundaries, binocular stereo vision technology was employed to measure the boundary distances. Field experiment results indicate that the system’s average relative errors of distance in ranging at distances of 25 m, 20 m, and 15 m are 6.72%, 4.80%, and 4.35%, respectively. This system is capable of meeting the real-time detection requirements for headland boundaries.

Intelligent residual film recovery machine monitoring and control system

Domestic residue recovery machines in China still follow the traditional towed mechanical model, lacking automation and intelligent features. Under the key R&D project of Xinjiang Autonomous Region, our team has developed integrated intelligent agricultural machinery for cotton straw crushing and residue recovery. This research covers the monitoring and control system, including PLC control and communication, working parameter monitoring and adjustment, algorithmic control of electro-hydraulic actuators, and human-machine interface display. Compared to traditional models, this machine replaces the conventional chain-driven mechanical structure and diesel tractor traction power with a fully hydraulic integrated suspension drive and power system. Sensors, PLC, and a touch screen are utilized for real-time monitoring and display of parameters. Hardware components like electromagnetic switch valves, electro-hydraulic proportional valves, hydraulic cylinders, and software elements such as PLC programs and intelligent control algorithms are employed for electro-hydraulic switching and proportional control of certain working parameters and mechanisms. All parameter information is consolidated in the PLC host and expansion modules and displayed and controlled on the human-machine interface. The system was installed on the first-generation machine for indoor testing, revealing that the monitoring and control system used on the first-generation machine achieves parameter detection and adjustment of the actuating mechanisms. It incorporates more advanced functions compared to traditional residue recovery machines. However, there is still ample room for improvement in the overall intelligence of the machine. The control system’s structure requires adjustment, and certain functionalities of the overall mechanical structure and working components need optimization or upgrading. Detailed enhancements to the entire system will be made in the next-generation machine.

Agriculture neutralization: perspective from intelligent agricultural machinery

Agriculture neutralization: perspective from intelligent agricultural machinery

Multi-machine collaboration realization conditions and precise and efficient production mode of intelligent agricultural machinery

Multi-machine collaboration realization conditions and precise and efficient production mode of intelligent agricultural machinery

Navigation path recognition between rows of fruit trees based on semantic segmentation

The navigation path recognition has been recognized as one of the most important subtasks of intelligent agricultural equipment in orchard operations. However, there are still some challenges in recognizing navigation paths between rows of fruit trees, including the accuracy, real-time performance, generalization of deep learning models. The Fast-Unet model was proposed by pruning and optimization based on Unet for recognizing navigation paths between rows of fruit trees, which inherited encoding–decoding structure and multi-layer feature sensing capability. The number of convolutional kernels used to extract features in the Fast-Unet was reduced to one-fourth of that in Unet to improve inference speed. To address the blurring of the boundary of the recognized object due to the reduction in the number of convolutional kernels, the atrous spatial pyramid pooling (ASPP) module was used in the encoding part to extract the multiscale information to improve the recognition accuracy. The navigation path edges determined by Fast-Unet and Canny operators, navigation lines and yaw angles were generated by the least square method.. In this study, the Fast-Unet model was first trained on the peach dataset, and then the trained model was transferred to the small dataset of oranges and kiwifruits for navigation path recognition to verify the generalization. The Mean Intersection over Union (MIOU) of the Fast-Unet for peaches, oranges and kiwifruits navigation path extraction accuracy were 0.977, 0.987 and 0.956, respectively. The mean difference between the predicted yaw angle of peaches, oranges and kiwifruits navigation paths and the labelled were 0.397°, 0.102° and 0.239°, respectively. In terms of real-time performance, the inference speed was 48.8 frames per second (FPS) to process the RGB image data on a single-core CPU. The inference speed of the Fast-Unet model was 1.59 times higher than that of Unet. Through transfer learning, the Fast-Unet model can realize real-time recognition of navigation paths for peaches, oranges and kiwifruits. These results can provide technical and theoretical support to the development of navigation equipment and visual slip prediction for intelligent agricultural machinery in the orchard.

Research and Implementation of Millet Ear Detection Method Based on Lightweight YOLOv5.

As the millet ears are dense, small in size, and serious occlusion in the complex grain field scene, the target detection model suitable for this environment requires high computing power, and it is difficult to deploy the real-time detection of millet ears on mobile devices. A lightweight real-time detection method for millet ears is based on YOLOv5. First, the YOLOv5s model is improved by replacing the YOLOv5s backbone feature extraction network with the MobilenetV3 lightweight model to reduce model size. Then, using the multi-feature fusion detection structure, the micro-scale detection layer is augmented to reduce high-level feature maps and low-level feature maps. The Merge-NMS technique is used in post-processing for target information loss to reduce the influence of boundary blur on the detection effect and increase the detection accuracy of small and obstructed targets. Finally, the models reconstructed by different improved methods are trained and tested on the self-built millet ear data set. The AP value of the improved model in this study reaches 97.78%, F1-score is 94.20%, and the model size is only 7.56 MB, which is 53.28% of the standard YoloV5s model size, and has a better detection speed. Compared with other classical target detection models, it shows strong robustness and generalization ability. The lightweight model performs better in the detection of pictures and videos in the Jetson Nano. The results show that the improved lightweight YOLOv5 millet detection model in this study can overcome the influence of complex environments, and significantly improve the detection effect of millet under dense distribution and occlusion conditions. The millet detection model is deployed on the Jetson Nano, and the millet detection system is implemented based on the PyQt5 framework. The detection accuracy and detection speed of the millet detection system can meet the actual needs of intelligent agricultural machinery equipment and has a good application prospect.

Research on an Intelligent Agricultural Machinery Unmanned Driving System

Intelligent agricultural machinery refers to machinery that can independently complete tasks in the field, which has great significance for the transformation of agricultural modernization. However, most of the existing research on intelligent agricultural machinery is limited to unilateral research on positioning, planning, and control, and has not organically combined the three to form a fully functional intelligent agricultural machinery system. Based on this, this article has developed an intelligent agricultural machinery system that integrates positioning, planning, and control. In response to the problem of large positioning errors in the large range of plane anchoring longitude and latitude, this article integrates geographic factors such as ellipsoid ratio, long and short axis radius, and altitude into coordinate transformation, and combines RTK/INS integrated inertial navigation to achieve precise positioning of the entire vehicle over a large range. In response to the problem that existing full-coverage path planning algorithms only focus on job coverage as the optimization objective and cannot achieve path optimization, this paper proposes a multi-objective function-coupled full-coverage path planning algorithm that integrates three optimization objectives: job coverage, job path length, and job path quantity. This algorithm achieves optimal path planning while ensuring job coverage. As the existing pure pursuit algorithm is not suitable for the motion control of tracked mobile machinery, this paper reconstructs the existing pure pursuit algorithm based on the Kinematics characteristics of tracked mobile machinery, and adds a linear interpolation module, so that the actual tracking path points of motion control are always ideal tracking path points, effectively improving the motion control accuracy and control stability. Finally, the feasibility of the intelligent agricultural machinery system was demonstrated through corresponding simulation and actual vehicle experiments. This intelligent agricultural machinery system can cooperate with various operating tools and independently complete the vast majority of agricultural production activities.

A Collaborative Path Planning Method for Intelligent Agricultural Machinery Based on Unmanned Aerial Vehicles

The development of agricultural farming has evolved from traditional agricultural machinery due to its efficiency and autonomy. Intelligent agricultural machinery is capable of autonomous driving and remote control, but due to its limited perception of farmland and field obstacles, the assistance of unmanned aerial vehicles (UAVs) is required. Although existing intelligent systems have greater advantages than traditional agricultural machinery in improving the quality of operations and reducing labor costs, they also produce complex operational planning problems. Especially as agricultural products and fields become more diversified, it is necessary to develop an adaptive operation planning method that takes into account the efficiency and quality of work. However, the existing operation planning methods lack practicality and do not guarantee global optimization because traditional planners only consider the path commands and generate the path in the rectangular field without considering other factors. To overcome these drawbacks, this paper proposes a novel and practical collaborative path planning method for intelligent agricultural machinery based on unmanned aerial vehicles. First, we utilize UAVs for obstacle detection. With the field information and operation data preprocessed, automatic agricultural machinery could be assisted in avoiding obstacles in the field. Second, by considering both the historical state of the current operation and the statistics from previous operations, the real-time control of agricultural machinery is determined. Therefore, the K-means algorithm is used to extract key control parameters and discretize the state space of agricultural machinery. Finally, the dynamic operation plan is established based on the Markov chain. This plan can estimate the probability of agricultural machinery transitioning from one state to another by analyzing data, thereby dynamically determining real-time control strategies. The field test with an automatic tractor shows that the operation planner can achieve higher performance than the other two popular methods.

Application of digital design technology in the design of intelligent agricultural machinery and equipment

Abstract Today, China has become the world’s largest agricultural machinery and equipment countries and the use of countries, agricultural machinery industry for more than a decade to maintain high growth, along with three revolutionary changes in agricultural technology, has initially ushered in the development of artificial intelligence stage. At present, relative to the weak foundation of China’s agricultural machinery standardization work and the requirements of high integration of technology and multidisciplinary integration of artificial intelligence, there are bottlenecks that constrain the problem. This paper makes a directional discussion on the direction of standardization breakthrough through the analysis of digital design technology and typical applications at home and abroad, the analysis of standard transformation of scientific and technological achievements, and the analysis of current problems. Aiming at technical standards development, implementation, and improvement to promote technological innovation, application, and improvement of intelligent manufacturing of agricultural machinery products. So that scientific and technological innovation continues to enhance the level of technical standards, technical standards continue to promote the transformation of scientific and technological achievements. Technical standards and intelligent agricultural machinery lend each other in international competition and become a strategic means to participate in international cooperation and competition. Accelerate the transformation of the old and new dynamics of the agricultural mechanization industry, and realize the standardization and internationalization of the intelligent agricultural mechanization industry.

Obstacle detection and tracking for intelligent agricultural machinery

The existing 3D lidar-based obstacle detection and tracking methods are inaccurate. Furthermore, as the number of obstacles increases, particularly in the agricultural machinery working scene with many machineries, ridges, and pedestrians, these methods become difficult to track. To address the aforementioned issues, a new obstacle detection and tracking method based on grid map is proposed. Firstly, a dynamic clustering algorithm that combines region-growing and density-clustering is developed. Secondly, geometric features and point cloud density are used to associates obstacles. Thirdly, a Kalman filter is designed to estimate the motion states of obstacles. Finally, a platform for agricultural machinery has been used to conduct comparative experiments. The proposed method improves detection accuracy by 4.86% and reduces average time by 47.8% when compared to Density Based Spatial Clustering of Applications with Noise (DBSCAN). And the tracking error of moving obstacles is less than 5%.

Research on High Precision Magnetic Positioning Technology Based on Facility Transport Platform

With the continuous development of economic globalization, the research demand for intelligent agricultural machinery equipment in modern agriculture is increasing. This paper, which aims at the positioning problem of mobile robots in agriculture production, proposes a low-cost magnetic positioning scheme for cement ground. First, the analytical magnetic field model of ground magnets was established. Then, by comparing the analytic computing results, simulation results, and measured values, the modified model of magnetic fields was built and the relevant impact factors were calculated. After that, acquisition devices were used to collect the ground magnetic field data for the establishment of a magnetic field matching algorithm. Finally, the result showed that the positioning displacement error was ±1 mm, and the positioning accuracy was higher than the conventional indoor positioning method, which solved the problem of the low indoor positioning accuracy of agriculture mobile robots and contributes to the efficient production and modernization of agricultural machinery equipment.

Real-time fruit detection using deep neural networks on CPU (RTFD): An edge AI application

Intelligent harvesting can greatly reduce the manual labor for fruit harvesting and is one of the main application areas of intelligent agricultural machinery. Smart edge sensors must detect the growth status of tomatoes and strawberries in real-time for intelligent harvesting to be possible. Due to edge devices' relatively low image processing power, it remains challenging to implement deep learning algorithms for real-time detection in field applications. Therefore, scholars are strongly interested in utilizing computationally small, accurate, and quick models for edge harvesting machinery. In this research, we propose RTFD, a lightweight algorithm for edge CPU devices that detects fruit. Based on the PicoDet-S model, RTFD optimizes the efficiency of real-time detection for edge CPU computing devices by enhancing the model's structure, loss function and activation function. The experimental results demonstrate that the mAP of RTFD for tomato and strawberry datasets at various growth phases is 0.78 and 0.96, respectively, with a computational effort of 1.44 GFLOPs. RTFD improves mAP by 1.9% and 2.3% on the tomato and strawberry datasets, respectively, with minimal loss in computation and model parameters, making it more suitable for edge device deployment. Meanwhile, we proposed an approach for 8-bit quantization-aware training of the Conv2D and Linear layers of the model for improved edge deployment. This approach quantizes 32-bit floating-point values in the Conv2D and Linear layers into 8-bit integers, thereby reducing the model's size and accelerating detection speed. The testing findings indicate that the quantized RTFD model has a size of 1.33 MB and a CPU detection speed of around 11 FPS. Using the quantified trained model, we developed the Android application RTFD-CPU to evaluate the model's performance in test scenarios. RTFD-CPU was assessed on the smartphone Redmi K30pro (Qualcomm Snapdragon 865 CPU and 8 GB of RAM) with a detection speed of 19 FPS utilizing only the CPU. In summary, the proposed RTFD has great potential for intelligent picking machines, and the concept of redesigning the model structure, loss function, and activation function, as well as training by quantization to speed up the detection of deep neural networks, is anticipated to be successfully implemented in edge computing.

Construction of Intelligent Chassis Assembly Line based on Machine Vision and Industrial Robot

The intelligent chassis assembly line integrates digital production control system, automatic loading and unloading system of heavy-duty robot, machine vision hole centering system, professional high-precision docking platform, robot automatic screw tightening system and quality control system. It realizes the automatic, intelligent and unmanned assembly process of the rear axle assembly of the intelligent agricultural machine chassis with the middle motor, the middle motor bracket, the front motor and the front motor bracket. The production line has been successfully put into use, creating the first intelligent assembly of intelligent agricultural machinery chassis in China.

Cost Changes and Technical Efficiency of Grain Production in China against a Background of Rising Factor Prices

Based on panel data for the inputs and outputs of the three major staple grains (rice, wheat and corn) in China from 2000 to 2020, we calculated the cost efficiency using the stochastic frontier cost function model and examined the effects of cost changes for the three major grains to explore the sources of cost increases for grain production. On this basis, the impact of the input factor structures on the technical efficiency of the three food grains was further analyzed under the conditions of price increases. We found that the labor prices and production costs showed the same trends of changes. Compared to 2000, the labor prices in 2020 increased 7.33-fold and the technical efficiency values for the three grains were all close to 0.9 (0.8689, 0.8912 and 0.8451). An efficiency decomposition showed that the adjustment effect of labor prices was the main factor in cost increases, but the effects of technological progress and efficiency improvement could effectively reduce the costs of grain production (the largest average value for technological progress was for rice at 0.4569). In comparison, the effects of technological progress on cost reduction were more obvious. By analyzing the influence of input factor structures on technical efficiency, it was found that the influence of different input factor structures on technical efficiency was heterogeneous among the different grains. This paper puts forward the following policy recommendations: first, improve the level of mechanization by developing social services to reduce the dependence on labor; secondly, promote the construction of agricultural informatization, such as accelerating the research and development of intelligent agricultural machinery and promoting the transformation of traditional agriculture to intelligent agriculture; finally, promote the marketization of land element price through land trusteeship to reduce the land transfer price.

Influencing Factors of the Distribution Accuracy and the Optimal Parameters of a Pneumatic Fertilization Distributor in a Fertilizer Applicator

A pneumatic fertilization distributor used for fertilizing in a fertilizer applicator is a key component of the applicator. The parameters of a pneumatic fertilization distributor affect the uniformity and accuracy of the fertilization of a fertilizer applicator. To obtain the optimal design parameters of a pneumatic fertilization distributor, a fluidstructure coupling simulation test and a bench test were carried out in the Intelligent Agricultural Machinery Laboratory of the Nanjing Institute of Agricultural Mechanization from March 2021 to July 2022. The curvature–diameter ratios of the elbow, bellow length, and air velocity were selected as the experimental factors, and the variation coefficient of the fertilizer discharge at each discharge outlet within 0.5–3 s was selected as the experimental index. A five-level quadratic regression orthogonal rotation combined test was carried out. The results showed that: (1) all three factors had a significant impact on the uniformity of the fertilizer discharge. The reasonable ranges of the curvature–diameter ratio, bellow length, and air velocity were 0.5–1.5, 350–550 mm, and 25–35 m/s, respectively. (2) The order of the influence of the three factors on the uniformity of the fertilizer discharge in descending order was as follows: the curvature–diameter ratio of the elbow, the bellow length, and the air velocity. When the bellow length was 460 mm, the curvature–diameter ratio was 0.6, and the inlet air velocity was 28 m/s. The uniformity of the fertilizer discharge was optimal. A pneumatic conveying system was redesigned according to the optimal parameters, and a bench test was carried out. The results showed that at different speeds, the coefficient of variation of each row’s displacement was not greater than 5%, and the simulation test results were consistent with the bench test results.

Construction method of high-horsepower tractor digital twin

Background: Agricultural machinery equipment is the core element of advanced agricultural productivity. The operation system of agricultural machinery equipment involves machine-human-environment-society interactions. Limited by the production mode, operation mode, etc., the design and development, manufacturing, operation and maintenance control, recycling and other links of agricultural machinery equipment are still independent of each other. The massive information in the agricultural production process has not been fully utilized, so there are some outstanding problems such as low operating performance, low production efficiency, and poor integration of agricultural machinery and agronomy. Methods: Focusing on the agricultural production process, this paper proposes the construction method for a high-horsepower tractor digital twin, and expounds on its operation mechanism. Taking high-horsepower tractor ploughing operation as an example, by deploying tractors, central servers, and an Internet of Things (IoT) platform, we developed a digital twin service platform for the agricultural production system of intelligent agricultural machinery equipment and built a tractor digital twin to verify the effectiveness of the proposed method. Results: The accuracy rate of the tractor ploughing quality prediction service based on this platform is 96.65%. Under open-loop control, the number of excellent, good, medium and poor sets of the tractor ploughing quality are 153, 955, 1470, and 1422, respectively. After adopting closed-loop control, the number of excellent and good sets increased by 378, and 821, respectively, and the number of medium and poor groups decreases by 119 and 1080, respectively. Through this platform, the operation quality can be effectively and accurately predicted and improved, which verifies the effectiveness of the proposed construction method of high-horsepower tractor digital twin. Conclusions: This research provides a method framework for the construction of the digital twin of tractor operation and maintenance control processes, and provides strong support for the vigorous development of intelligent agriculture.