In order to minimize the total unproductive operation time and waiting time of unproductive operation, a multimachine and multitask collaborative optimization scheduling model was constructed in this paper to solve the problems such as unreasonable operation path planning of combine harvester and inability of coordinated optimization scheduling between combine harvester and grain truck, and a multiMachine Cooperative Optimal Scheduling algorithm (MMCOSA) was designed. Firstly, the static path planning scheme of the combine harvester was calculated by improving the traditional ACO algorithm. Then, the relative distance nearest strategy was adopted to realize the dynamic optimization of cooperative operation between combine harvester and grain truck. The experimental results showed that the total nonproductive operation time and nonproductive operation waiting time calculated by MMCOSA algorithm in this paper were both 17.5% and 19.02% shorter than the results of traditional ACO algorithm. MMCOSA algorithm not only accelerated the convergence rate, but also shorted the operation time, which could provide an effective solution to the cooperative scheduling problem of combine harvester and grain truck in busy farming season.

Germplasm is the foundation of agriculture and a national strategic resource. The quality of seeds affects the production scale and potential of the overall planting industry. There are some problems in Chinast seed industry, such as incomplete and opaque industrial chain information and imperfect and unsafe seed traceability systems. This paper integrates blockchain technology into traditional seed information traceability and proposes a seed quality traceability model for distributed data storage. This paper uses Ethereum, Internet of Things, and terminal applications to design a threelayer architecture: application layer, Internet of things layer, and data storage layer. According to the characteristics of Ethereum and the requirements of system data storage and query, a distributed multidatabase storage mode is designed. The system stores the data information of each link from seed production to the circulation department. Combined with the characteristics of blockchain data information that cannot be tampered with, high transparency and traceability, it ensures that all data in the seed supply chain will not be changed or hidden by others to realize accurate traceability and rapid identification of seed quality. The system uses the smart contract algorithm to directly hit the source of the seed data to ensure the quality and safety of the purchased seeds and to provide information technology and service guarantees for safeguarding the rights and interests of seed consumers.

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.