Abstract
Agricultural diseases and insect pests are one of the most important factors that seriously threaten agricultural production. Early detection and identification of pests can effectively reduce the economic losses caused by pests. In this paper, convolution neural network is used to automatically identify crop diseases. The data set comes from the public data set of the AI Challenger Competition in 2018, with 27 disease images of 10 crops. In this paper, the Inception-ResNet-v2 model is used for training. The cross-layer direct edge and multi-layer convolution in the residual network unit to the model. After the combined convolution operation is completed, it is activated by the connection into the ReLu function. The experimental results show that the overall recognition accuracy is 86.1% in this model, which verifies the effectiveness. After the training of this model, we designed and implemented the Wechat applet of crop diseases and insect pests recognition. Then we carried out the actual test. The results show that the system can accurately identify crop diseases, and give the corresponding guidance.
Highlights
As a superpower with more than 20% of the world’s total population, China has been facing the problem of insufficient arable land resources
Image recognition of crop diseases and insect pests can reduce the dependence on plant protection technicians in agricultural production, so that farmers can solve the problem in time
This problem can be solved by convolution operation in convolutional neural network
Summary
As a superpower with more than 20% of the world’s total population, China has been facing the problem of insufficient arable land resources. The area of crops affected by pests and diseases in China is as high as 280 million km every year, and the direct yield loss is at least 25 billion kg [1] In recent years, this problem is on the rise and seriously threatens the development of planting industry. Image recognition of crop diseases and insect pests can reduce the dependence on plant protection technicians in agricultural production, so that farmers can solve the problem in time. This paper tries to build the Internet of Things platform in the complex environment of mountainous areas, and carry out the research on the identification model of crop diseases and insect pests The purpose of this model is to improve. VOLUME 8, 2020 agricultural informatization, deal with the harm of pests and diseases to crops, and improve crop yield
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