In order to effectively identify the types of fruits and improve the efficiency of commercial deep processing of fruit industry and offline fruit sales channels, this paper proposes a fruit image recognition method based on the combination of semantic segmentation model （U2-Net） and ResNet-50 model to realize automatic fruit image recognition. U2-Net is used to segment the binary image of fruit, and then the white pixel value in the binary image is changed into the real color value of fruit by OpenCV algorithm. Finally, the fruit image is recognized by ResNet-50. The results show that the accuracy of Alexent, VGG16, GoogLeNet and this model on the training set is 99.66%, 99.65%, 99.9% and 99.8%, and the accuracy of the verification set is 96.5%, 99.9%, 99.6% and 100%, respectively. The result showed that the fruit image recognition method proposed in this paper can effectively extract the color, shape, texture and other features of fruits, thus realizing accurate recognition of different kinds of fruit images.

In order to increase the economic return of maize cultivation and expand the promotion degree of maize threshing machinery, this paper aims to improve the threshing and netting rate of maize and reduce the breakage rate of threshing process, and designs an adjustable maize threshing device by studying the structural characteristics of maize threshing device. Through theoretical analysis, the diameter of the threshing device drum was determined to be 600 mm, the length of the grain rod was 1 400 mm, and the length of the adjusting rod was 150 mm. Through mechanical and kinematic analysis of the threshing process, the variable gap of the concave plate screen was derived to be 0-25 mm, and then the threshing gap was derived to be 20-45 mm. According to the design of the threshing drum, the operating parameters were determined, the speed of the threshing drum was 200-300 r/min and the material feed is less than 2.1kg/s. A physical prototype of the adjustable corn threshing device was tested with the adjustment gap, threshing drum speed, and material feed as the test factors, and the threshing rate and breakage rate as the indexes, and the parameters were optimized based on the test results. The results showed that the significance of the factors influencing the corn cleaning rate and kernel breakage rate was as follows: adjustment gap > threshing drum speed > material feed. The results of the validation tests showed that the optimal combination of parameters was 30 mm adjustment gap, 300 r/min threshing drum speed, and 1.5 kg/s material feed rate, and the optimal combination of parameters resulted in 99.07% and 2.23% of corn cleaning rate and kernel breakage rate, respectively, meeting the agronomic requirements of the grain threshing process. This study also provides a reference for the design and optimization of corn threshing equipment.

In view of the difficulties of farmers and grassroots plant protection personnel in identifying crop diseases, the identification model is established by using VGG16 and Resnet50 for 18 crop diseases of apple, corn, grape and tomato as the research object. Through data pretreatment, data enhancement, model parameter optimization and model cross validation, the single crop multidisease identification and multi crop multidisease identification models are constructed. The performance comparison results show that VGG16 has better recognition performance than Resnet50, and the recognition accuracy of VGG16 model is more than 96%. After analyzing the VGG16 recognition model, it is found that the recognition performance of the single crop multidisease identification model has the best recognition performance. Therefore, based on the method of establishing single crop multidisease identification model, combined with smart phone, Web technology and network programming technology, this paper is proposed to develop an intelligent identification system of crop diseases. The system can provide users with accurate identification results, disease knowledge and prevention methods. The Socket network service of the system can be used as an independent module to provide a unified interface for crop disease identification for agricultural robots, intelligent agricultural machinery, unmanned aerial vehicle, agricultural expert systems, and so on. This study can provide technical support for the informatization and intellectualization of agricultural plant protection.

Aiming at the problems such as excessive redundant information, small proportion of target area and randomness of target location in the collected corn grain images, a new corn grain image slicing method based on color space (HSV) threshold segmentation was proposed in this paper, which improved the recognition accuracy of damaged and mildewed corn   grain. Firstly, the segmentation threshold was determined, the corn grain contour was extracted, and the cropping  coordinates were determined and the image was clipped according to the vertex coordinates of the minimum external rectangle frame of the contour. Secondly, the weight of GoogLeNet model without image slicing processing and with image slicing processing were obtained. After the first, second and third convolution layer and inception5b module of GoogLeNet, the Grad-CAM visualization method was used to visualize the features extracted from different convolutional layers. Finally, based on the accuracy of verification set, the ability of the two models to extract intact, damaged and mildewed corn grain features was evaluated. The results showed that the method proposed in this paper could improve the verification set accuracy to 93.74%, which was 7.99% higher than that of the data set without image slicing processing on GoogLeNet. The models concerned areas were displayed by the Grad-CAM visualization method, and the features extracted from the network were interpreted visually, which verified the effectiveness of this method and provided a new idea for the pretreatment and identification of corn kernel image.

Research work on maize is always limited by the short harvesting period and the high labor intensity of harvesting. The pressure feed wheels in the maize peeling device are made of rubber and can deform greatly in practice, making it difficult for ANSYS and EDEM software to build a flexible working simulation model for them. In order to address the above problems, a flexible simulation model of the maize peeling device is established in this paper by coupling EDEM and RecurDyn. The simulation is based on three operating conditions with peeling roll and press wheel speed as test factors. The results show that when the speed of peeling roller is 480 r/min, the speed of pressure feed wheel is 96 r/min, and the feeding speed is 500 kg/h, the average force of corn cob is 15.65 N, the variance of the force is 7.80, and the rotation speed of cob around its own axis is 58.86 r/min, which is the most stable force and fast rotation speed of corn under the working condition. With the cob peeling rate, seed drop rate and kernel breakage rate as the test index for bench test to verify the simulation, when the peeling roller speed of 480 r/min, the pressure feed wheel speed of 96 r/min, feeding speed of 500 kg/h, the cob peeling rate of 90.22%, the seed drop rate and kernel breakage rate of 0.97% and 1.07%, peeling rate at the same time, the drop rate and kernel breakage rate of lowest. Breakage rate were the lowest, and the experimental and simulation results coincided with each other. This study shows that the use of EDEMRecurDyn coupling to establish a discrete simulation model of the corn peeling device can provide preliminary guidance for the subsequent test work of the corn peeling device, with compressing the test time and reducing the labor burden.

 The vegetable picking robot plays a crucial role in the mechanization, automation, and intelligentization of vegetable production. This paper summarizes and analyzes the research status and key technologies of vegetable picking robots at home and abroad, including optimizing end effectors to improve picking efficiency and enhancing recognition accuracy through advanced image processing algorithms and deep learning models. The characteristics and suitable scenarios of three types of end effectors for vegetable picking robots, namely gripperbased, suctionbased, and biomimeticbased, are analyzed. The composition and structure of the visual system in vegetable picking robots are discussed, and three recognition methods are compared: traditional image processing methods based on color and texture features, machine learning methods such as Kmeans clustering and support vector machine (SVM) algorithms, and deep learning methods such as YOLO, Faster RCNN, and SSD networks. Based on the growth environment and characteristics of different vegetables, suitable recognition methods are summarized and compared in terms of recognition performance. Additionally, issues related to the target of operation, working environment, hardware constraints, and production costs of vegetable picking robots are identified, followed by prospective analysis on vegetable cultivation patterns, software systems, hardware systems, and leveraging regional characteristics.

At present, most of the fruit and vegetable picking is mainly based on manual picking, which has disadvantages such as low efficiency and large picking cost, while the problem of labor shortage restricts the rapid development of agriculture as the population aging problem becomes more and more serious. The endeffector, as a key component of fruit and vegetable picking robot, largely affects the picking rate and damage rate of the picking robot, and the research on the endeffector is of vital significance. The current research status of fruit and vegetable picking robots at home and abroad is fully described. The endeffectors of picking are summarized according to the different picking and driving methods, and the causes of damage in the picking process are summarized. By citing typical endeffectors of picking, we analyze the causes of fruit damage during the picking process. By comparing the specific parameters of existing picking robot endeffector solutions, the problems of inaccurate identification and positioning, low picking efficiency, etc. are presented, and the future endeffectors are prospected in terms of damage rate and picking efficiency.

In order to clarify the influence of impact load on damage and fracture characteristics of walnut during primary processing, in this paper, based on the continuum damage theory, the existence of damage was first proved by uniaxial compression test. Then, the damage and fracture models of walnut were established by single impact and repeated impact loading tests using the drop hammer impact test bench. Finally, the influence of walnut size between 30mm and 44mm on model characteristic parameters was discussed. The results showed that the damage accumulation coefficient was the minimum value 3.89 when the walnut size was 35mm, when the walnut size was 30mm, the threshold energy required for cracking was 4.74J/kg. The walnut fracture was caused by the damage of walnut. The energy threshold is the judgment standard for walnut crack generation, and the damage accumulation coefficient quantifies the damage resistance ability of walnut. The threshold energy is linearly with the increase in the walnut size. The relationship between the walnut size and damage accumulation coefficient conforms to polynomial function. The results are helpful to control walnut fracture during primary processing.

The physical parameters of chili seeds are important inputs in the discrete element simulation. In order to improve the reliability and accuracy of the numerical simulation, the physical property parameters of capsicum seeds were measured experimentally, including triaxial size, density, Poissons ratio, friction coefficient, recovery coefficient, et al. Combined with the “Hertz Mindlin with JKR” viscosity model in EDEM, surface energy parameters were introduced to conduct the angle of repose test. The three most significant influencing parameters (chili seedseed rolling friction coefficient, chili seed surface energy, and chili seedsteel plate rolling friction coefficient) were selected among the influencing factors by PlackettBurman test, and the regression model of the three significant influencing parameters and the angle of repose was established to study the influence of the significant factors on the angle of repose under the interaction effect. The interval of significant parameters was divided into 5 levels by gradient average for climbing test to get the optimal range of values, and finally the response surface method (BoxBehnken) was applied to analyze the variance of the quadratic polynomial of the regression model in terms of the angle of repose, the relative error and the three important parameters, and the model was optimized with the minimum value of relative error to get the optimal combination: rolling friction coefficient between chili seeds was 0.75, JKR surface energy was 0.31J/m2, and rolling friction coefficient between chili seeds and steel plate was 0.60. The optimal combination of parameters obtained from the calibration test was used to conduct the simulated stacking test, and the average angle of repose was 26.71°, and the relative error was 4.61%.

In order to change the low efficiency mode of manual bag removal and mechanical crushing of waste bacteria rods, a small crushing separator integrating bag removal and crushing was designed. The debag device and the crushing device were designed, and the force of the auger on the waste bacteria rod during the crushing process was analyzed, based on the discrete element software EDEM, the binding model of the waste rod was established, and the grinding process was simulated. Through the design of orthogonal test, the influence of the parameters of the auger on the grinding degree of the waste rods was obtained and the prediction model was established. Through response surface analysis and prediction model, the parameters affecting the grinding degree of waste bacteria rods were optimized. The best working parameters were obtained as follows: the rotating speed of the grinding shaft was 323.4r/min, the outer diameter of the auger was 261.185mm, and the thickness of the blade was 6mm. The model predicted that the grinding degree of waste bacteria rods was 81.739%. The best working parameters were substituted into the simulation test, and the relative error between the predicted value of the model and the simulated value was 1.79%, which verified the availability of the prediction model. The prototype was manufactured with the outer diameter of the auger of 260mm and the thickness of the blade of 6mm, and the crushing test was carried out. The results showed that when the rotation speed of the auger was 320r/min, the working performance of the whole machine was stable, the feed of the waste bacteria rod was smooth, the crushing qualification rate was 92%, and the bacteria bag removal rate was 93%, which met the design requirements of the waste bacteria rod crushing separator.

“Huxiang F2” Shiitake spawn sticks during the color change period upon debagging were taken as the test object, and their physical and mechanical properties were investigated. The density of Shiitake spawn sticks was measured using the sand discharge method, yielding a value of 0.64g/cm3. The dynamic friction coefficients between spawn sticks and spawn bags, as well as between spawn sticks and 65Mn spring steel, were determined to be 0.485 and 0.584, respectively, employing the slope method. The attachment strength and strain tearoff force averaged at 1.01kPa and 25.69N, respectively. A radial compression test was performed on the Shiitake spawn sticks using an electronic universal testing machine. Combining the experimental results with theoretical calculations, when the loading speed of the spawn sticks was 10mm/min, the average radial compressive strength of the rods was found to be 44.86kPa, with corresponding average values of elastic modulus and Poissons ratio of 0.48MPa and 0.17, respectively. These research findings establish a fundamental theoretical foundation for the design of key components and the determination of operating parameters for Shiitake spawn stick debagging equipment.

Chinas tea industry is still a laborintensive industry. Among them, fresh leaf picking consumes a lot of labor, which is the most laborintensive and labortime link in tea production. The mechanized tea harvesting is the only way for my countrys tea development. Starting from the current situation of tea picking in my country, this paper summarizes the tea picking standards and agronomic requirements of machine picking in China. The research and application status, advantages and disadvantages of single tea picking machine, double tea picking machine, riding tea picking machine and tea picking robots are analyzed from the perspective of onetime picking principle and selective picking principle respectively. The problems that restrict the realization of comprehensive mechanized tea picking in my country are analyzed,and the development opinions such as the deep integration of agronomy and agricultural machinery, the combination of basic research and advanced technology, and the improvement of the versatility of tea machinery are proposed in the future related to tea production in China, so as to provide reference for the further research of tea picking equipment in China.

Aiming at the problem of low screening rate (high soil content) of FUM， triaxial mean size and critical damage pressure of FUM and soil were measured, by means of FUM and soil aggregate Statistical analysis of triaxial dimensions and mechanical properties test. The results showed that the triaxial size of FUM and the soil aggregates on the screen was not significant, and the critical extrusion force value of FUM and the soil aggregates on the screen was significant. Based on this conclusion, the efficient separation scheme of FUM and soil aggregates by screening and kneading method was proposed. The efficient separation test device for FUM was constructed. The optimum range for calculating the kneading force of screening and kneading device was 20-40N, and the value range of the kneading monomer thickness was 0.2-0.4cm. The quadratic regression orthogonal rotation test scheme was designed, the results showed that the influence of the feeding amount, the square term of the feeding amount and the square term of the conveying screening speed on the screening rate was very significant, and the kneading force , the interaction between feeding amount and the speed of the conveyor screening had a significant impact on screening rate. Feeding amount and the square term of the speed of the conveyor screening had significant effects on damage. Kneading force, interaction between feeding amount and conveyor screen speed, interaction between feeding amount and kneading force, and the square term of feeding amount had significant effects on damage. By DesignExpert.V8.0.6 software, the optimal parameter combination was obtained: feeding amount of 0.68kg/s, conveyor screen speed of 0.38m/s, kneading force 36N, screening rate of 80.5%, damage rate of 12.6%, respectively. Field tests showed that the average screening rate of the separator was 79.18%, and the average damage rate was 13.34%, which could meet the agronomic requirements of mechanized harvesting of FUM.

According to the corridor environment and vegetation distribution in tomato greenhouse, SLAM and navigation algorithm of robot multi‑sensor fusion are optimized. Firstly, based on the Cartographer algorithm, the dedistortion processing of LiDAR data was carried out, and then the unscented Kalman filter (UKF) was used to fuse the information of LiDAR, odometer and IMU to optimize the robot pose estimation, and the optimized algorithm was used to construct a high‑precision map of the greenhouse. The established high‑precision raster map is verified by the improved A* and DWA fusion algorithm, and the search efficiency and path safety of the A* algorithm are improved by dynamically adjusting the weights and optimizing the search logic, so that the robot can find the optimal and safe path more intelligently in the greenhouse environment, and the effectiveness of the algorithm is verified in the gazebo platform of ROS and the greenhouse environment in the field. The experimental results indicated that the average position deviation was 10.3 cm when the running speed of the robot was not more than 0.6 m/s, which met the operation requirements of the tomato greenhouse.

Leaf vegetable output in our country is huge and has the considerable economic value. At present, artificial harvesting is the main method to harvest leafy vegetables, which restricts the development of industry seriously, so it is imperative to carry out the research on mechanized leaf vegetable harvesting. In order to understand the development status of the harvesting technology and equipment for leafy vegetables at home and abroad, this paper classified leafy vegetables and non-leafy vegetables, expounded the performance characteristics, applicable environment, working principle and technical advantages and disadvantages of key components such as cutting, conveying and so on of the representative models of leaf vegetable harvesting equipment at home and abroad, and analyzed the research status of profile modeling and automatic alignment of cutting table. It is concluded that the rootcutting consistency of the cutting parts of the existing nonheading leafy vegetables under soil cutting machine in our country is poor, and the clamping and conveying device damages the leafy vegetables greatly, and the problem of the standardized planting model is not formed, finally, a series of suggestions are put forward to carry out study on the shovel cutting parts under soil, strengthen the research on the basic physical and mechanical properties of leafy vegetables, and promote the development of the whole mechanized industrial chain.

“Double carbon” has become the key task of Chinas agricultural green development. It is urgent to reduce agricultural greenhouse gas emissions in order to achieve the carbon neutral goal of 2060. This paper reviews the research progress and development trend of agricultural carbon emission in China in recent years, summarizes the research on the six major emission sources of agricultural materials, rice planting, carbon fixation of cultivated land, straw burning, livestock and poultry breeding and agricultural machinery, and publishes the suggestions and prospects for carbon emission reduction of the six major agricultural emission sources. The research shows that there are many empirical analysis on the agricultural carbon emission intensity of the five major agricultural emission sources of agricultural materials, rice planting, straw burning, livestock and poultry breeding and agricultural machinery, and the carbon emission reduction effect is significant, because of the effective regulation of its carbon emissions and the impact of strengthening the purchase subsidy policy. However, there is less empirical analysis on the carbon sequestration of arable land, and the process of carbon emission reduction is slow, mainly due to the greater impact of the subjective factors of farmers, so we should strengthen the publicity and learning of farmers. Although Chinas total agricultural carbon emissions fluctuate up and down, they tend to decline as a whole. In order to achieve green and sustainable development, the government and the whole people need to work together. The implementation of lowcarbon agriculture is conducive to achieving the 2060 carbon neutral goal.