Crop Disease Image Recognition Based on Improved VGG Network

With the rapid development of computer technology, using machine vision to identify crop diseases has become a trend. However, the current research on crop disease image recognition mainly focuses on improving the recognition accuracy, and seldom considers the robustness research under the actual complex natural conditions. In the actual complex natural conditions, noise and background of complex natural conditions will reduce the recognition accuracy. Therefore, in this paper, VGG network is improved by adding high-order residual and parameter sharing feedback sub network into VGG network to identify crop diseases under actual complex natural conditions. The feature expression of crop disease appearance is provided by high-order residual sub network, which makes the accuracy rate of disease recognition higher. The background noise in deep feature of disease image is weakened by parameter sharing feedback sub network, which makes the improved VGG network have stronger robustness. The experimental results show that the proposed method is better than SVM, alexnet, resnet-50 and vgg-16 in recognition accuracy and robustness.