Tea rolling pressure is an important factor that affects the quality of tea rolling. Realtime detection of tea rolling pressure is the key to realize automatic tea processing. In this paper, based on the analysis of the force situation of the related components of the rolling pressure and the distribution of the rolling action area, the SW-DAMS coupling simulation method was used to analyze the contact mechanical characteristics and force changes of the sensing sensors corresponding to different rolling pressure detection methods, and the best detection point of the rolling pressure was located at the upper end of the gland. At the same time, the structure and measuring range of the pressure sensor were determined, and the rolling pressure detection device was designed to complete the pressure calibration and detection test. The results showed that when the standard rolling speed was 45 r/min, the selected annular pressure sensing sensor had an internal diameter of 32 mm, a height of 20 mm, connecting flange height 40 mm, a pressure output accuracy of 0.1%, and no overshoot. The productivity of the prototype was 65 kg/h, and the broken tea rate was 1%. The dynamic pressure changed in real time between 3-35 N with the rolling process, which could meet the requirements of automatic tea production line for testing the rolling pressure.

In order to improve the performance of welfare breeding equipment for cattle and sheep grazing on grassland, an intelligent drinking water temperature control system for livestock breeding based on multisensor insitu data detection and fuzzy PID control is developed. The system is mainly composed of data acquisition module, water injection heating module, temperature control module and remote data monitoring module. The data acquisition module uses a variety of sensors to collect the water temperature, water quality, water volume and other information in the drinking water device in real time. The water injection heating module adopts the form of bottom water injection heating, which facilitates the laminar flow of water temperature from bottom to top and makes the water temperature distribution more uniform. The temperature control module adopts the fuzzy PID control algorithm based on closed loop negative feedback, which can selfadjust the PID coefficient to achieve accurate water temperature control. The remote data monitoring module is designed for users, so that users can observe the working conditions of drinking water devices in real time. The fuzzy PID temperature control test and water temperature field uniformity test verification and simulation analysis were carried out for the device. The test results showed that the fuzzy PID temperature control effect was smoother and the heating response speed was faster than the conventional PID temperature control effect. When the preset water temperature target temperature was 23 ℃ and the inlet water temperature was 7℃, 9℃, 11℃, 13℃ and 15 ℃ respectively, the maximum fluctuation of the actual heating temperature and the target temperature was only 0.3 ℃. By heating different temperature measuring points, the maximum temperature difference of different temperature measuring points on the same horizontal plane is 0.3 ℃, the temperature difference of water in different sections of the water tank is less than 0.2 ℃, and the water temperature at the temperature measuring points of the device can be accurately controlled at (23±0.5)℃. The experiment shows that the water temperature field of the drinking water temperature control device is uniform and can provide equipment support for welfare breeding.

Agricultural machinery outsourcing operation service is an important part of Chinas agricultural socialization services, which is important for improving agricultural production efficiency and promoting agricultural modernization. Based on the research data of 975 farmers in Sichuan, Jilin and Jiangsu provinces, this paper empirically tested the influence of social networks on the adoption behavior of farm machinery outsourcing services and its mechanism of action by using Heckman twostage model and mediating effect model. The results showed that in the process of land preparation and harvesting, social networks could not only encourage farmers to adopt agricultural machinery outsourcing services, but also enhance their adoption level. Specifically, for every 1% increase in social networks, the adoption rates of agricultural machinery outsourcing services for farmers in land preparation and harvesting processes could increase by 53.1% and 57.9%, respectively, and the adoption level could increase by 6% and 7.7%, respectively. Different types of social networks had differentiated impacts on the adoption behavior of agricultural machinery outsourcing services by farmers. Vertical social networks had the highest level of impact, every 1% increase could increase the adoption rates of farmers in the land preparation and harvesting stages by 35.3% and 47.4%, respectively, and an increase of the adoption level by 4.5% and 7.5%, respectively. The impact of oblique social networks is secondary, with every 1% increase promoting an increase in adoption rates of 6.8% and 10.8% for farmers in the land preparation and harvesting stages, and an increase in adoption level of 1.1% and 1%, respectively. The impact of horizontal social networks was the weakest, with every 1% increase promoting an increase in adoption rates of 1.8% and 1.3% for farmers in the land preparation and harvesting stages, and an increase in adoption level of 0.1% and 0.1%, respectively. Risk preference plays a partly mediating role in the relationship between social networks and farmers adoption behavior of farm machinery outsourcing services. Accordingly, this paper proposes to cultivate and broaden farmers vertical and horizontal social networks, improve the platform of rural land management rights transfer, and enhance the development level of agricultural service outsourcing market.

Plant protection unmanned aerial vehicles (UAVs), advanced and efficient modern agricultural equipment, have developed rapidly in China in recent years. In 2017, it was included in the subsidy scope of agricultural machinery purchase by pilot provinces and cities for the first time. However, because of the short implementation time of the subsidy policy, there are many unreasonable factors. This paper summarizes the development and subsidy status of UAVs for plant protection implemented by pilot provinces and municipalities and the Ministry of Agriculture and Rural Affairs since 2017, and summarizes relevant classification and classification policies. Based on the summary, this study points out the problems in basic configuration parameters and subsidy amount setting, including unequal quality and subsidy amount, too little limitation on basic parameters, not highlighting highend products in the classification, and not considering remote areas in the subsidy. Finally, the study suggests that the subsidy policy of plant protection UAVs will develop towards unifying quality appraisal, refining classification parameters, highlighting intelligent technology, precise subsidy standard, and setting subsidies according to actual situations of different regions.

Under the background of green and sustainable development strategy, the application of straw deep turn and returning to the field is of great value to the development of highquality ecological agriculture and the control of agricultural and rural environmental pollution. Based on the theory of the anchor effect of behavioral economics, this paper uses the micro survey data of 1036 farmers in 4 counties of Henan Province to analyze the influence mechanism of the anchor effect on the adoption of straw returning technology using the Probit model and OLS model. The results show that when only external anchors exist, there is a significant external anchor effect on farmers adoption of straw deep turn and returning to the field, which is indicative of the dominant role of the intuitive decision system and operates in a passive processing mode. When both external and internal anchors coexist, the internal anchor effect becomes more prominent, with regression coefficients of 0.630 and 0.088, and the extrinsic anchor effect weakens or disappears. In addition, the degree of commercialization plays a positive moderating role in the external anchor effect on the adoption of straw deep turn and returning to the field, with the interaction term coefficient of 2.174 and 0.061. The higher the degree of commercialization, the stronger the promotion effect of the internal anchor effect on farmers adoption of straw deep turn and returning to the field. Similarly, social networks have a positive moderating effect internal anchor effect on farmers, with interaction term coefficients of 0.641 and 0.121. Social networks help the longterm operation of straw deep turn and returning to the field technology. Based on these findings, it is proposed to improve farmers awareness of green technology, moderately expand the scale of agricultural machinery service, and use digital media to promote the concept of green development to improve the farmers enthusiasm to adopt straw deep returning to the field.

Fusarium Head Blight (FHB) of wheat is one of the most terrible diseases that lead to wheat yield reduction. It is of great significance to carry out automatic identification research on wheat FHB. However, the traditional methods of segmentation and recognition of wheat scab under complex field background is generally carried out through threshold value, color histogram, etc., and its segmentation and recognition accuracy is poor and its generalization ability is not satisfactory. In order to quickly and accurately segment FHB scab and then effectively confirm the severity of the disease for assisting agricultural workers to carry out subsequent researches, this paper proposes a semantic segmentation network model, UNetA, based on UNet structure and attention mechanism for wheat FHB. The wheat ear pictures are augmented and then input into the convolution layer of UNetA model for extracting feature map. The Attention mechanism consists with position attention and channel attention after convolution layer to make further extraction, and then EncoderDecoder structure with skipconnection and BN layer makes up the rest part together. The whole network utilizes crossentropy loss with weighted parameters to balance the gap between classes and to measure the difference between predicted label and actual label. Subsequently, UNetA model is compared with the stateoftheart methods. The experiment results show that the proposed method performs favorably against others in terms of MIoU in the same configuration and obtains an 83.90% of MIoU. Moreover, the proposed method spends 0.588 0 s time for wheat sacb segmentation, shorter than others.

Aiming at the application scenario that the feeding operation in pond culture requires uniform coverage of the whole pond, there are problems of high artificial feeding intensity and low feed utilization rate, an automatic navigation control system for breeding vessels capable of adapting to different polygonal shape ponds was designed. The control system used a lowcost Beidou positioning module and a highprecision electronic compass for integrated navigation. The position and heading information of the pond culture vessel was obtained as the input of the navigation controller. The path of the navigation process was realized by the builtin navigation controller based on the PD algorithm. A polygon echo line navigation path planning algorithm was designed to quickly realize the navigation path planning of the polygon shape pond. The pond navigation test was carried out. The test results showed that: with the designed automatic navigation system, the culture vessel could sail according to the planned route. When the water speed was 0.4-0.5 m/s, the maximum error after stable tracking was less than 2.62 m, the average tracking error was less than 1.30 m, and the navigation accuracy met the automatic feeding requirements of pond culture.

The identification of crop disease is related to crop yield and quality, and it is an essential part in the development of intelligent agriculture. With the rapid development of deep learning in the field of image processing, the method of identifying crop disease types from images by deep learning has gradually become the mainstream. In this paper, we mainly review the methods of crop disease recognition based on deep learning, briefly introduce deep learning and convolutional neural network, and collect some common public disease image datasets. According to the different collection environment of training sample, we summarize the progress of deep learningbased disease identification methods in recent years from two aspects of laboratory and field, point out their advantages and disadvantages of each method, and conclude that there are three main problems in this research field such as insufficient data, difficult task and complex network structure of deep learning model. On this basis, we propose that the establishment of largescale, multispecies, and multitype disease database and the design of highperformance deep learning model are important development directions in the future.

 In order to solve the problems of low accuracy and coupling of various environmental factors, the multifactor coordinated regulation technology of fungi house environment under the factory cultivation mode of edible fungi was studied. According to the environment requirements of edible fungus for temperature, humidity, gas and light, a set of container type fungi house was built, and a fungi house environmental control system based on Internet of Things technology was developed. The functions of field sensing data acquisition, device control, data storage, remote access were realized. The multifactor cooperative regulation algorithm of air conditioning and new fan was mainly studied. In the cultivation experiment of morchella, the control effect was tested. The results showed that the temperature control deviation of fungi house was less than 0.4℃ when the air conditioner was operating in cooling mode, the temperature control deviation of fungi house was less than 0.9℃ When the air conditioner was operating in heating mode, The humidity control deviation of fungi house was less than 4%, the carbon dioxide concentration control deviation of fungi house was less than 32μmol/mol. Temperature, humidity, carbon dioxide concentration and light intensity all met the agronomic requirements of industrial cultivation. The experiment proved that the environmental regulation system of fungi house with multifactor coordination regulation algorithm could further improve the precision of edible fungi cultivation.

To solve the problem of traditional agriculture vehicle in environmental protection, power performance, a new type of multifunctional agricultural vehicle with electric fourwheel drive was developed and tested for its traction performance. In the power system scheme, the rear drive axle was driven by one central motor, the front drive axle used two independently driven hub motors, and the system matching design was carried out according to the operation of agricultural vehicles and driving conditions. The tractive operation dynamics of the whole machine were analyzed, and the traction characteristics and economic analysis were carried out. The results showed that the electric agricultural vehicle had good traction performance and economy. In terms of traction performance, the maximum traction force of the machine of rear wheel drive was 1 925 N and the maximum traction efficiency was 74%. In terms of economy, the energy consumption per unit area and the cost per unit area of intertillage were reduced to 42.4% and 80.1%, respectively, of that of traditional oil tractor. The electric agricultural vehicle meets the needs for green and environmentfriendly new agricultural production in places such as greenhouses and facilities such as facility agriculture and sightseeing leisure agriculture while also providing a reference for the design of new electric agricultural vehicles.

The key to rural revitalization lies in industrial revitalization, characteristic industries that based on regional resource endowments and historical culture are the important part of rural industries. Nanjing is the capital city of Jiangsu Province which have excellent economic conditions. Its land presents typical hilly landform features, the area of low hills and gentle hills accounts for 608% of the total land area, and the water area exceeds 11%. Unique geographical location and resource endowment determine that Nanjing will inevitably develop “green + efficient” urban characteristic agriculture. In the context of the continuous reduction in the supply of agricultural labor and the deepening of the aging population, it is an inevitable trend to vigorously develop characteristic agricultural mechanization, to promote green, efficient, intelligent and integrated development by mechanization. Based on data analysis, combined with the onsite survey in Nanjing, this article analyzed the status of production and primary processing mechanization of Nanjings characteristic agricultural, found out the main problems in mechanization development currently, and proposed countermeasures from the aspects of land scale, management organization, production standardization, industrial integration, technology and talent support, etc.

It is an important way to solve the shortage of rural labor force and reduce the cost of fruit picking to realize mechanized intelligent picking in orchards. Accurate identification of fruit in orchards is the key technology. We took jujube as the research object. In order to establish a maturity identification model suitable for multiple varieties and strong practicability, the jujube fruits of many varieties in natural environment were divided into mature fruit, immature fruit and ripe fruit, semired fruit and immature fruit labeling methods, and four recognition models based on YOLO V3, YOLO V4, YOLO V4-Tiny and Mobilenet-YOLO V4-Lite were established. The study showed that both YOLO V3 and YOLO V4-Tiny models in the YOLO algorithm could be applied to the two labeling methods, and the verification set mAP was about 94%, which proved that the YOLO algorithm could effectively identify the maturity of jujube fruits.

In order to realize the fast and accurate optimization of corn seeds, a watershed algorithm combined with convolutional neural network was proposed to detect the quality of corn seeds with different quality as the research object. Firstly, the watershed algorithm was used to divide the single corn seed, and then the quality of each seed was classified by the convolutional neural network model. According to the position of the single seed obtained by the watershed algorithm, the results were labeled in the image to realize the quality detection of seeds. The improved InceptionV3 model was used to test the seeds. The test results showed that the average accuracy rate, the average recall rate and the F1 value (harmonic average evaluation) of the two kinds of seeds with good quality and defects were 94.18%, 94.61% and 94.39%. Meanwhile, in order to highlight the performance of the convolutional neural network model, the results were compared with the traditional machine learning method, and the F1 value of the convolutional neural network model was 20.39% higher than that of the LBP+SVM model.

This paper aims to solve the problems of low recognition rate of potato leaf diseases and difficulty in localization of late blight spots in the natural environment based on potato leaf images collected in the field environment. Firstly, the potato leaf disease was identified using five neural network models: ALEXNET, VGG16, InceptionV3, RESNET50, and MobileNet. The results show that the InceptionV3 model has the highest recognition accuracy, which can reach 98.00%. Secondly, the late blight spots of potato leaves were detected, and an improved CenterNet-SPP model was proposed. The model obtains the center point of the object through the feature extraction network and then obtains the image information such as the offset of the center point and the size of the target through the regression of the center point. The mAP of the trained model under the verification set was up to 90.03%.Using F ₁ as the evaluation value analysis, compared with other objective detection models, the CenterNet-SPP model has the best effect, whose accuracy is 94.93%, recall rate is 90.34%, F ₁ value is 92.58%, and the average detection time of an image is 0.10 s.This paper provides a relatively comprehensive deep learning algorithm and model research basis for potato disease recognition and detection in the natural environment.