Energy saving and emission reduction is the key to improve quality and efficiency, transformation and upgrading of intelligent agricultural machinery and equipment. Hybrid tractor is the key model for the development of the global new energy agricultural machinery and equipment industry, and it is also the key technology path to accelerate the transformation of Chinese agricultural machinery to high-end, intelligent and green. In recent years, domestic and foreign combined with local agricultural production characteristics, market demand-oriented, continued to spawn, iterative new technology and new products, and through multi-scene, multi-level demonstration and application, a complete technology research and development and application system was gradually formed, which accelerated the promotion of product maturity. On the basis of outlining the development history and development characteristics of hybrid tractors, the development status of key technologies in this field was summarized and analyzed from four aspects: design and development, power control, intelligence and test verification, which focused on analyzing the technological differences between domestic and foreign models, and the key issues restricting the development of the field were pointed out. Finally, under the background of policy guidance and technological innovation synergy, it looked forward to the development direction of the hybrid tractor industry from the perspective of agricultural modernization empowered by artificial intelligence, big data and other emerging technologies, and puted forward its future development trend in the optimized design of the whole machine, collaborative optimization and control, special performance test equipment, intelligent operation control and other aspects.

In response to the issue that traditional communication network cabling methods are unable to meet the requirements of high efficiency, stability and functional expansion for information transmission in autonomous hydrogen fuel cell tractors, a multi-rate control area network (CAN) bus architecture was proposed based on the SAE J1939 protocol and ISO 11783 standard. Firstly, the data flow requirements for sensing, positioning, drive control, etc. were analyzed in combination with the autonomous operation process in the field. Then, using the principle of minimum complexity in graph theory, multiple topologies were compared and a gateway plus three-bus structure was determined. The CAN communication network nodes were divided based on the structure of the autonomous operation hydrogen fuel cell tractor and its working conditions, and the functions of each node were defined. For common network topologies, a mathematical model was established based on graph theory, and the total information volume per second N and the node information volume per second Z of each structure were analyzed and compared. A high-speed and low-speed dual-bus structure was adopted to design the communication network of the autonomous operation hydrogen fuel cell tractor, with bus rates selected as 500 kb/s and 250 kb/s. The communication message and system software design were developed based on the SAE J1939 protocol and ISO 11783 standard. The hardware functions and communication effects of the communication system were tested by using CANoe software and verified on the whole vehicle. The test results of the bus load rate were 28.22%, 19.20% and 22.85%, all below the 50% requirement. These results verified the feasibility and provided a feasible solution for the design of the communication network of autonomous operation hydrogen fuel cell tractors.

The development of new energy power equipment for agriculture is of great significance for promoting the green and low-carbon transformation of agriculture, ensuring national food security and achieving agricultural modernization. A systematic review of domestic and international research progress in new power equipment for agricultural was provided. It focused on analyzing the current status and development trends of four major categories of equipment: new energy tractors, new energy micro-tiller, new energy work platforms, and new energy agricultural low-altitude aircraft. In the field of new energy agricultural machinery, tractors are developing in parallel toward pure electric, hybrid, and clean fuel options; new energy micro-tiller are evolving from “pure electric dominance” to “electric and hybrid parallel development and intelligent self-propulsion”. New energy work platforms are advancing along a technical path characterized by “electric drive as the main power source, supplemented by hydrogen/biofuels, and multi-form coordination of wheels, tracks, and wheeled legs”. New energy agricultural low-altitude aircraft are developing along a technical route featuring “electric drive as the main power source, hydrogen energy for extended range”. However, the new energy agricultural equipment industry is currently facing challenges such as inadequate adaptation of core technologies, an incomplete standard testing system, weak industrial chain coordination, lagging infrastructure, and an incomplete market service system. To this end, it was proposed that future efforts should be coordinated across multiple fronts: top-level policy design and standard testing, breakthroughs in key “three-electric” technologies and specialized chassis systems, industrial chain collaboration and pilot-scale validation, energy-supply infrastructure such as fast charging, battery swapping, and hydrogen fuel, and the completion of scenario-based demonstration and service systems. It was suggested to enhance the adaptability and reliability of new energy agricultural machinery to promote the application of electric agricultural machinery.

Hilly and mountainous areas are important grain production bases in China. The farmlands in these regions are characterized by small plot sizes, steep slopes, and narrow roads. Traditional fuel-driven chassis have disadvantages such as slow power response, inflexible steering, and low levels of intelligence. To address these issues, an electric intelligent chassis suitable for light and simple rice and wheat harvesters was designed. This machine adopted a distributed design to achieve flexible steering and a wide track design to adapt to rice harvesting operations in hilly and mountainous areas. Motion condition analysis was conducted, and the parameter design of the electric drive system and battery system was completed, solving the problem of slow power response of fuel-powered chassis. The software and hardware design of the motion control system was carried out, enabling remote control operation or autonomous navigation of the chassis. Performance tests and operation parameter optimization tests were conducted. Taking the harvesting speed, threshing drum speed, and cleaning fan speed as test factors, and the breakage rate, impurity rate, and loss rate as evaluation indicators, a Box-Behnken test was designed. The results of the Box-Behnken test were analyzed by variance, and a regression model between the evaluation indicators and the test factors was established. With the goal of minimizing the breakage rate, impurity rate, and loss rate, the optimal solution for the harvesting speed, threshing drum speed, and cleaning fan speed was obtained: the harvester's harvesting speed was 0.8 m/s, the threshing drum speed was 1 000 r/min, and the cleaning fan speed was 2 786.15 r/min. Field tests showed that under this condition, the breakage rate was 0.42%, the impurity rate was 4.05%, and the loss rate was 0.87%. The machine operated smoothly during the operation period, and the operation indicators met the standards for mechanized rice and wheat harvesting. The test results indicated that the designed electric intelligent chassis could meet the operational requirements of the harvester.

Aiming to address the issues of resource waste and environmental pollution caused by uniform fertilization in traditional methods, as well as the inadequate adaptability of existing variable-rate fertilization systems' lag compensation approaches, a prescription map-based variable-rate fertilization system for rice and wheat featuring spatio-temporal coupling compensation function was designed. The system employed an STM32F407 chip and CAN bus architecture for the control unit and central processor. Integrated with ArcMap GIS platform, it proposed a digital prescription map generation method based on agronomic expert decisions. The developed human-machine interface software supported GPS message reception/parsing, prescription map loading, and high-efficiency retrieval. Innovatively, a spatio-temporally coupled calibration model for fertilizer application adjustment was established through dynamic outlet positioning modeling and lag time self-learning algorithm, enabling automatic field calibration and dynamic compensation of system response time. Bench tests demonstrated that the self-learning algorithm achieved over 90.5% accuracy across working speeds (4~8 km/h) and fertilization gradients (0~450 kg/hm², 0~750 kg/hm², 450~750 kg/hm²). Field trials showed that with spatio-temporal calibration enabled, the average fertilization accuracy reached 97.4% within 50-meter segments—an 8.0 percentage points improvement over non-calibrated status—while maintaining only 2.5 percentage points accuracy fluctuation at 8 km/h. This system provided a high-precision, low-cost intelligent solution for variable-rate fertilization in rice/wheat cultivation, offering technical support for China's national policy of reducing fertilizer use while increasing efficiency.

The tractor serving as the core power equipment for achieving agricultural modernization and efficient production, has a technological development level that directly influences the efficiency, cost, and sustainability of agricultural operations. The industry is rapidly evolving toward high-end, intelligent, and green directions, with new power transmission technologies becoming the most critical and dynamic area of innovation in this transformation. A comprehensive review of the current research status of new power transmission technologies for tractors, both domestically and internationally was provided, which systematically addressed the field from three dimensions: theoretical research on new transmission systems, development of key technologies, and industrial applications. It was focused on diverse technical pathways, including power shift transmission, hydro-mechanical continuously variable transmission, pure electric drive systems, as well as diesel-electric hybrid, hydrogen hybrid, and methane hybrid power systems. It delved into the basic structure, working principles, and performance characteristics of these power transmission systems, and the development status of key technologies was analyzed by examining typical tractor products from China and other countries, along with their practical application outcomes and productization paths. The critical bottleneck issues that needed to be overcome in current new power transmission technologies were generalized and summarized, primarily including battery capacity and charging rates, control precision and stability, environmental adaptability of electric drive systems, multi-energy source management, and the production and storage of new energy sources. Finally, it outlined future development trends for new power transmission systems in tractors, proposing deep integration in three major directions: global energy efficiency utilization, full-scenario self-adaptive compatibility, and modular multi-functional expansion.

Aiming to address the key issues of low irrigation water use efficiency and backward management levels in grape cultivation in arid valley regions. It focused on collecting big data on the habitat and physiology of grape crops, integrating agronomic theoretical data, and an integrated decision-making system for water and fertilizer management was designed based on comprehensive multi-data coupling analysis. The system utilized standard Modbus protocol sensors for data collection and employed a LORA low-power wireless networking transmission module with freely settable timing. By integrating devices such as cloud boxes, gateways, and DTUs, the system established a soil and environmental Internet of things (IoT) monitoring and collection system, achieving continuous online monitoring and data collection of soil and the environment. The LORA wireless network transmission adopted the Modbus-RTU protocol for data transfer. The system incorporated a crop cultivation model built with an optimized fuzzy PID control algorithm based on the revised PM model. Through this model, the system can drive decision-making for irrigation and fertilization management, optimizing the agricultural production process. It achieved online control of integrated water and fertilizer irrigation equipment, reducing the need for manual operation. Experimental results showed that compared with traditional PID and fuzzy PID, the system's overshoot was reduced by 13.6 percentage points and 3.6 percentage points respectively, the system response rate was increased by 58.2% and 26.6% respectively; water usage was reduced by 25.0% and 13.5% respectively, and fertilizer usage was reduced by 30.0% and 17.1% respectively. This system aimed to promote the upgrade of integrated water and fertilizer technology, transforming the decision-making mechanism from “theory-oriented, experience-guided, and fixed-value regulation” to a benchmark based on big data analysis and judgment, with the expectation of providing technical support for the realization of real-time and precise regulation of integrated water and fertilizer management in grape plantations in arid valley regions.

In order to explore the suitability of abandoned cultivated land for recultivation and improve the utilization rate of cultivated land, taking Lishu County, Jilin Province as the study area, firstly based on the Google Earth Engine platform and the 2022 Sentinel-2 image data of Lishu County, a total of 46 features such as spectral features, vegetation features, texture features, and terrain features were calculated, and random forest algorithm was used to construct the optimal feature set, and finally 39 features were screened to participate in the classification, so as to obtain the scope of abandoned cultivated land. Then the AHP-CRITIC-TOPSIS model was used to construct an evaluation model of the suitability of abandoned cultivated land for recultivation from three dimensions: natural, soil and socio-economic conditions, and the suitability was divided into four grades: highly suitable, moderately suitable, barely suitable and unsuitable, so as to put forward a feasible scheme for the rehabilitation of abandoned cultivated land. The results showed that the accuracy of the classification results of land use types in Lishu County in 2022 was relatively accurate, and the overall classification accuracy and Kappa coefficient were above 95%, indicating that the classification results were consistent with the actual land types and the overall classification effect was good. The area of abandoned cultivated land not suitable for recultivation in Lishu County was 1 673.73 hm², which was mainly located in the southeast of the study area, and the area of abandoned cultivated land suitable for recultivation was 1 071.67 hm², which was mainly located in the low-altitude plain area in the northwest of the study area. At present, the implementation of the national cultivated land protection policy indeed improved the use of cultivated land in rural areas, but the problems of abandonment and low utilization efficiency of rural cultivated land were still prominent, and the results can provide a scientific basis for improving the quality of cultivated land and ensuring food security in Lishu County and surrounding areas.

Wheat, as one of the world's essential food crops, requires timely diagnosis and control of diseases and pests to significantly reduce yield losses, which is crucial for global food security. However, deep learning methods typically rely on large amounts of training data and high-performance computing resources, which pose limitations in scenarios with small sample sizes and limited resources. To address these issues, a knowledge-enhanced lightweight multi-modal wheat diseases and pests identification model named multi-modal blend convolutional neural network (Blend-CNN) was proposed. The model was structured around a dual-branch convolutional neural network framework. It utilized an EfficientNet backbone network to extract image features of diseases and pests and incorporated a multi-branch TextCNN backbone network to extract textual features of diseases and pests descriptions, thereby obtaining more feature information and improving identification accuracy. Additionally, an innovative convolutional network-based multi-modal fusion method was introduced, allowing the model to optimally integrate information from both modalities globally. Furthermore, to mitigate the accuracy loss common in traditional multi-modal methods, the gradient blend loss function was developed. Finally, to verify the model's effectiveness, comparative experiments were conducted on a constructed dataset containing 880 samples. The results demonstrated that the proposed model achieved the highest identification accuracy of 96.95% on this dataset, compared with other models, it had fewer parameters, lower complexity, and it was more lightweight which was applicable for edge devices, offering theoretical support for wheat diseases and pests identification in scenarios with limited sample sizes and resources.

Aiming at the problem that the artificial setting of comprehensive coefficient is difficult to adapt to the complex and changeable working environment of soil conditions in the process of tractor electro-hydraulic suspension tillage depth control, a tillage depth control method based on optimal draft-position comprehensive coefficient was proposed. Firstly, the test platform of tractor electro-hydraulic suspension draft-position integrated tillage depth control and loading system was built and the system was modeled by system identification method. Then aiming at the problem of uncertain parameters of the equivalent transfer function of the system, a global sliding mode control algorithm was designed, and an arc-tangent function was introduced to reduce the chattering of the system. The full factor sample test was designed, and the comprehensive operation evaluation index was established by compromise programming method. The expected draft-position comprehensive coefficient under different working conditions was obtained, and the optimal draft-position comprehensive model was established based on the least squares support vector machine optimized by particle swarm optimization algorithm (PSO-LSSVM). Finally, the effect of the control system was verified by using the built control system test platform. The results showed that the proposed algorithm achieved a rise time of 1.06 s without noticeable chattering. Compared with the fixed weight-depth control method with a comprehensive coefficient of 0.5, the proposed method reduced the depth deviation rate by 19.5% and 13.9% at target depths of 140 mm and 220 mm, respectively, while improving depth uniformity by 16.6% and 11.2%. This method can realize the adaptive adjustment of the comprehensive coefficient according to the soil specific resistance and tillage depth, and was more adaptable to the complex field operation environment, which provided a reference for the research of tillage depth control method.