Industrial production of edible fungi is the future development direction of China’s edible fungi industry. At present, the industrial production of edible fungi in China is dominated by wood-rotting fungi, and its production process includes bottling, sterilization and a series of links until the end of bottle digging. However, after bottle digging, the bottle cleaning still adopts manual cleaning, which leads to low washing efficiency and high labor costs. Based on analyzing the requirements of bottle washing in the industrial production of edible fungi, this paper introduces the working principle of bottle washing machines in the industrial production of edible fungi and designs the control system of bottle washing machines on this basis. Around the hardware and software system, this paper analyzes the functions and main model selection of the drive module, actuator module, sensor module, and designs the pneumatic drive system. According to the process of the bottle washing machine, using Guangyang SH−32R as a programmer, the electrical schematic diagram is drawn, and the control program of the bottle washing machine is written in level language. The trial production and test show that the bottle washing machine runs smoothly, the average pressure of the nozzle is 0.33 MPa, the water consumption of each bottle is 0.1 kg, and the single cleaning time is 10 s, which meets the requirements of industrial bottle washing.

Deep learning models are widely used in the field of crop detection and recognition. Their advantage lies in optimizing the model by constructing different functional perception layers, which can automatically extract features from input data and achieve end-to-end learning. However, the unknown data processing process in this model leads to a lack of interpretability, which becomes the main obstacle to the application of deep learning. To overcome the shortcomings of insufficient interpretability in deep learning models, researchers have proposed an interpretability method based on class activation mapping. This article summarizes the research progress of the class activation mapping algorithm Grad-CAM in crop disease classification and detection, crop pest detection and recognition, crop variety classification, target crop detection, and other applications. It explains the advantages of the class activation mapping algorithm in visualizing convolutional neural networks with arbitrary structures, and analyzes the shortcomings of class activation mapping algorithms such as low interpretation precision, unstable gradients, lack of evaluation standards, and single application background. It proposes the development trend of building models with high accuracy and interpretability, construction of new interpretive algorithms, establishing unified evaluation standards for interpretable algorithms, and ensuring the correctness of interpretable algorithms.

To strengthen the country, agriculture must be first strengthened, and only by strengthening agriculture can the country be strong. The report of the 20th National Congress of the Communist Party of China takes “basically realizing agricultural modernization” as one of the overall development goals of China in 2035. Comprehensively promoting rural revitalization and accelerating the construction of agricultural power, are the strategic deployment of the Party Central Committee in order to comprehensively build a modern socialist power. Since the reform and opening up, Nanjing’s agriculture has developed rapidly and its agricultural competitiveness has improved rapidly. However, compared with the developed agricultural areas in China, the agricultural competitiveness is not strong. The number of agricultural employees in Nanjing is only 210 000, with an average annual decrease of 20 000. The per capita cultivated land is 0.025 hectares, which is only 25% of the national average. Brand awareness is not enough, and it suffers from fierce competition in the industry. Based on the diamond model analysis framework, this paper analyzes Nanjing’s agricultural competitiveness from five aspects: production factors, demand conditions, related supporting industries, enterprise strategic structure and industry competition, government, and opportunities. This paper puts forward five strategies to improve the competitiveness of modern agriculture, including talents, science and technology, capital, land, and enterprise competitiveness, to give full play to the advantages of human resources in Nanjing, continuously optimize the environment of human resources, and do a good job in the internal allocation and external introduction of agricultural talents. Relying on innovation carriers such as agricultural high zone and agricultural innovation Park, accelerate the integrated innovation of key technologies in leading agricultural industries such as efficient facility vegetables, economic forest fruits, and agricultural product processing, and promote the“going out” and “bringing in” of Nanjing agriculture; establish a diversified agricultural investment system guided by financial support for agriculture to provide financial support for the development of modern agriculture and comprehensively enhance the competitiveness of agricultural enterprises. Through the promotion of agricultural competitiveness, the agricultural modernization of Nanjing will be accelerated, so as to provide strength to help China build an agricultural power.

Seedling pick-up mechanism play an important role in the transplanter of dry land, which determines the quality, reliability and efficiency of transplanting. It is the key to realize transplanting automation. Based on the study of the current situation of seedling collecting mechanism at home and abroad, this paper introduces several typical seedling collecting mechanisms of transplanting machinery in dry land. They are connecting rod seedling picking mechanism, planetary gear system seedling picking mechanism, pneumatic seedling picking mechanism, ejector rod seedling picking mechanism, clamping seedling picking mechanism and transplanting seedling picking robot. Their advantages and disadvantages are compared from the aspects of seedling picking speed, complicated structure and cost. Automatic seedling collecting mechanism is the most suitable seedling collecting mechanism, while robot seedling collecting mechanism will be the final choice. At present, the seedling picking mechanism is still developing, and it is developing in the direction of high speed, high efficiency and intelligence.

Under the background of vigorously developing herbage harvesting machinery in China, in order to adapt to the complex ground environment of grass rake operation in Inner Mongolia, reduce the transport area of machinery and tools, and improve work efficiency, the overall structure of BL-600 finger-disc rake and its main component of spring teeth with stress analysis and connecting rod mechanism are mainly carried out in this paper in order to solve the problems, such as large volume of machine transport, difficulty in turning around and easy deformation and breakage of spring teeth. Proe/Mechanica module modeling and simulation methods are used to analyze the stress of the spring teeth, so as that reliable and effective spring teeth materials are determined according to the simulation results. The structure and position of the working machine and the stroke of the oil cylinder are determined through the analysis of the motion of the grass rake. In addition, it improves the adaptability of the whole machine to the working environment. At the same time, the experiment verifies that the leakage rate of the rake is 2%, which meets the design requirement. Through experimental research, it is found that the model has stable performance, strong practicability, and promotion value.

With China’s urbanization, land circulation has emerged as an avenue for efficient and intensive land use. Embracing technology as a primary driver, the unmanned farm technology model represents a bold attempt at sustainable agriculture development. This review focuses on the frontier and development trajectory of key core technologies, and addresses the significant industrial challenges of insufficient accumulation of data on unmanned farms, unknown interaction mechanisms among the environment, plants, and equipment, and lack of multi-parameter integration and regulation strategies for intelligent equipment. Technologies such as autonomous positioning and navigation, online professional sensor, work obstacle information perception, path planning, decision-making, multi-machine collaboration, autonomous operation, and variable operation technology have been implemented in this study. The research shows that, in future, realizing unmanned farms requires special sensors for agricultural intelligent equipment, precise operational decision control systems, and practically intelligent equipment. At meanwhile, addressing such challenges necessitates a focus on core technologies such as insufficient basic big data accumulation of unmanned farms, modeling of environment-plant-equipment interaction mechanism, and intelligent decision control algorithm. This comprehensive improvement in automation, intelligence, and environmental sustainability of agricultural production marks the future development trajectory of unmanned farms.

Aiming to solve the problems of insufficient capacity and high consumption of electric tractors, an optimal control strategy of composite power supply system based on battery plus super capacitor was proposed. By analyzing the topological structure of the electric tractor complex power system and the resistance characteristics of the electric tractor under various operating conditions, a complex power system structure scheme suitable for electric tractor was selected and a test condition suitable for electric tractor was designed. At the same time, the power demand of the electric tractor drive motor under the test condition was determined. According to the charging and discharging characteristics of the main power components of the electric tractor composite power system, the relevant mathematical model was established, and the simulation model of the electric tractor complex power system was built in Simulink. In addition, according to the load demand of the electric tractor under the test condition and the discharge characteristics of the complex power supply system, a fuzzy control strategy of the complex power supply system was formulated. Taking the minimum current at the maximum power discharge of the battery as the optimization goal, the improved genetic algorithm was used to optimize the fuzzy control parameters. The models of the composite power supply system under different conditions such as single power supply and logic threshold control strategy was built for comparative simulation. The results showed that: compared with the single power supply, logic threshold control strategy and fuzzy control strategy, the peak current battery with the improved genetic algorithm can be reduced by 63%, 62.9% and 43.9% respectively, the average current was respectively reduced 24.8%, 3.2% and 8.5%. It is also shown that this strategy is beneficial to reduce the peak current battery and prolong the service life of the battery.

Agricultural machinery autonomous navigation is an effective approach to achieve precision agriculture, and high-precision path tracking control is a critical assurance for the reliability of intelligent agricultural machinery autonomous operations. To improve the adaptability of path tracking algorithms under different path curvatures and initial error conditions, a geometric path tracking combined algorithm based on adaptive switching of two geometric path tracking methods was proposed. The steady-state errors and convergence characteristics of the pure pursuit (PP) and Stanley model in U-shaped path tracking were analyzed, and their optimal geometric path tracking parameters were respectively set at a constant velocity. A path tracking method switching logic was designed based on tracking errors and path curvature as input, and a boustrophedon path was used to test the geometric path tracking combined algorithm. A mobile cart platform was built to verify the effectiveness of the proposed algorithm. The tracking results showed that when the initial error is 2.5 meters, the Stanley model had a faster convergence rate than the PP algorithm. When operating at speeds of 1.0 m/s for straight paths and 0.7 m/s for curved paths, the PP and Stanley model exhibited similar tracking errors for straight segments, but when the curvature changed abruptly, the maximum tracking errors for the PP and Stanley model were 12.0 cm and 11.0 cm respectively. The path tracking combined algorithm effectively reduced the tracking error during curvature changes, with a maximum tracking error of 9.0 cm under the same speed configuration for the reciprocating shuttle path, representing a reduction of 25.0% and 18.2% compared to the PP and Stanley model respectively. The integration of geometric-based path tracking algorithm reduced the computational load and complexity of online optimization for forward distance and gain coefficient, and thus effectively improved the adaptability of path tracking algorithms for agricultural machinery under complex soil condition.

With the continuous development of information and intelligent manufacturing technology, agriculture has entered the era of intelligent and automated production. Unmanned smart farms, as an important direction of modern agricultural development, are facing opportunities and challenges. As one of the key supporting technologies for unmanned farms, unmanned aerial vehicle (UAV) swarm flight control technology provides crucial technical support for agricultural production in fields such as field inspection, livestock management, and irrigation control. This study aimed to analyze the data interaction methods of various components in UAV swarm simulation flight control, establish and optimize a multi-rotor UAV swarm flight simulation environment to meet the application requirements of UAV swarm flight control in unmanned smart farms. In this paper, by analyzing the interaction logic among the ROS system, PX4 flight controller, MAVROS communication module, and Gazebo simulation environment in UAV swarm simulation flight control, we built a multi-rotor UAV swarm flight simulation environment based on the open-source XTDone simulation platform. We also realized model construction and flight control of UAV swarm based on the ROS system, PX4 flight controller, and Gazebo. Furthermore, using a laser radar to collect environmental information, we optimized software algorithms in the ROS distributed framework, achieving simultaneous localization and mapping (SLAM) based on scan matching algorithm and navigation based on optimal path planning algorithm. Theoretical simulation and experimental results demonstrated that the platform had advantages such as open-source, low cost, scalability, and modularity. The constructed simulation environment can achieve UAV swarm flight control, construction of 2D maps in enclosed environments, and autonomous navigation flight. Analysis revealed that the unit flight accuracy of the UAV swarm under this simulation platform was approximately 76%, and the regression model constructed from cumulative flight distance and flight error had an R² of 0.830 9. The research results demonstrated the feasibility of using UAV formation collaborative flight operations to meet the common agricultural operation needs in unmanned smart farms, demonstrated the application effect and advantages of UAV formation in unmanned smart farms, and provided technical ideas for deep optimization of simulation flight environments and expansion of modern agricultural production application scenarios. It also showed certain reference value and reference value in research and practice in related fields.

In order to solve the problems of high difficulty and low efficiency of manual counting in scientific research of cotton aphid population prediction and control, a cotton aphid image recognition algorithm based on YOLO neural network was proposed and developed into software. First, the images of artificially inoculated cotton aphids were taken by mobile phone for 15 consecutive days, 50 clear images were selected and cut into 6 sub-images, and then the training set and test set were obtained by using LabelImg software for manual labeling. Then, 10 models from the series of YOLOv5 and YOLOv8, whose training parameters were set as the same (batch size was 32, iteration was 100 rounds, initial learning rate was 0.01, and periodic learning rate was 0.01), were selected and trained by using the server of the AutoDL platform. Finally, the trained models were tested, and the YOLOv8l model showed the best overall performance, with mAP50 reaching 0.926. In order to provide users with convenient and easy-to-use man-machine software, the front end of the software was developed by using PYQT5, to realize the functions of reading and counting of cotton aphid pictures, visualizing results and exporting results to Excel. The back end of the software adopted an image processing method of "splitting-detection-merging", which ensuring the efficient detection of YOLO model on small targets. After testing, the software had an average precision of 0.945 for counting dead cotton aphids and live cotton aphids, which was comparable to manual counting and had good practical value. This research may provide an intelligent detection tool for researchers related to cotton aphid control and also provided key operation information for precise operation in scenes such as unmanned farms.