Disease is one of the main factors affecting crop growth. How to reflect the external morphological features of the disease and completely retain the color and texture information of the disease area is one of the key research issues for crop disease segmentation. Meanwhile, aiming at the problem of low segmentation accuracy with traditional convolution neural network-based methods in the crop disease leaf image, this project pro     ses a spatial pyramid-oriented encoder-decoder cascade convolution neural network for crop disease leaf segmentation. The network consists of a region disease detection network and a region disease segmentation network. Region disease detection network is a kind of network combining cascade convolution neural network with spatial pyramid. This method connects the three-level convolution neural network model, where the structure of the three-level neural network model varies from simple to complex. Different crop disease leaf features are extracted from the different neural network levels. And images are screened to complete the detection of crop disease leaf. What’s more, a space pyramid pooling layer is added to each network level. This pooling strategy does not require fixed size input, which increases the size selection of input model. The region segmentation network is established based on the Encoder-Decoder structure. The multi-scale convolution kernel is used to improve the local receptive field of the original convolution kernel and accurately segment the crop disease leaf area. Finally, we conduct experiments on the crop disease leaf images under different conditions, the results show that the proposed method has higher segmentation accuracy. In terms of Precision, Correct segmentation, over-segmentation and under-segmentation indexes, etc.,. Moreover, it can meticulously reflect the external morphological features of the crop disease leaf and relatively better retain the color and texture information

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