Research on Recognition Model of Crop Diseases and Insect Pests Based on Convolutional Neural Network

Abstract

Most of the traditional detection methods for crop diseases and insect pests are manually operated in the field according to the experience and technology of the staff, which have the disadvantages of long time and low efficiency. With the development of deep learning technology, the application of complex deep neural network algorithm models in the field of crop diseases and insect pests can effectively solve the above problems, however, the current research on the identification method of crop diseases and insect pests only focuses on the identification and analysis of single crop diseases and insect pests, and does not analyze and improve the analysis and improvement of various crops. Therefore, this paper proposes a recognition model of crop pests and diseases based on convolutional neural network. First, on the bilinear network model, the ResNet50 network is used as the feature extractor, that is, the backbone network of the network, instead of the original VGG-D and VGG-M backbone networks. Secondly, a connect module is added to design the bilinear network model and the extractor to do mutual outer product with the previous features of different levels, so that it is connected with the outer product of the feature vector. Finally, the loss function is used to conduct experiments on the AI Challenger 2018 crop pest and disease dataset. The experimental results show that the average recognition rate of the improved B-CNN-ResNet50-connect network model reaches 89.62%.