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, convolutional 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 ten crops. In this paper, Inception-ResNet-v2 model is used for training. The experimental results show that the overall recognition accuracy is 86.1%. The experimental results verify the effectiveness of the proposed model.
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