关键词: 卷积神经网络, 图像分割, 作物病害, 级联卷积神经网络

Abstract:

In order to solve the problem of low segmentation accuracy of traditional convolution neural network in crop disease leaf image, a method of crop disease leaf image segmentation based on cascade convolution neural network is proposed.The network is composed of regional disease spot detection network and regional disease spot segmentation network. The regional disease detection network is constructed based on the traditional VGG16 model, and the global pooling layer is used to replace the fully connection layer, so as to reduce the model parameters and realize the accurate location of leaf disease spot. The region segmentation network is established based on the structure of encoder-decoder model. The local receptive field of the original convolution nucleus is improved by using the multi-scale convolution kernel, and the disease region is segmented accurately.The segmentation experiments are carried out on the diseased leaf images in different environments, the segmentation accuracy is 87.04%, the recall rate is 78.31%, the comprehensive evaluation index value is 88.22%, and the segmentation speed of single image is 0.23 s.The experimental results show that this method can meet the needs of leaf image segmentation of crop diseases in different environments, and can provide reference for further research on crop disease identification methods.

Key words: convolutional neural network, image segmentation, crop diseases, cascade convolutional neural network