A crop disease image recognition algorithm based on feature extraction and image segmentation

Abstract

Due to the complex background of the field, it is a highly complex and flexible task to recognize and diagnose diseases from crop images. Image processing and machine vision can adapt to the complex and changing natural scenes, laying the basis for recognition and diagnosis of crop diseases. This paper designs and verifies an image segmentation method and a disease recognition method for crop disease images under complex background. The segmentation method was developed by improving graph-cut segmentation algorithm with saliency map and excess-green method, while the recognition method was designed based on a single hidden-layer forward neural network (NN). Experimental results show that our segmentation method outperformed he traditional graph-cut algorithm, and fuzzy c-means (FCM) clustering in segmenting the fine-grained disease images, and that our recognition method could accurately identify typical leaf diseases with high stability. The research results provide a good reference for the application of image processing and machine vision in disease image processing.