Abstract:

The purpose is to develop a grading system for the disease course of rice blast based on machine vision technology, so as to realize accurate and objective classification of the disease course of rice blast. Based on GrabCut, gaussian filter, OTSU binarization, color space conversion, threshold cutting, etc., an algorithm model of rice blast classification judgment was proposed. The algorithm model is implemented by OpenCV and python, and the reverse threshold cutting is taken as the core strategy to separate the leaves from the diseased spots, and then the percentage of diseased spots is calculated by the cyclic traversal model to realize the rapid and accurate classification of rice blast. The result of the algorithm model matches the manual judgment by professional researchers up to 95.77%. Compared with the manual judgment, the algorithm model has higher stability and objectivity. At present, the classification of disease course of rice blast mainly depends on the experience judgment of researchers. Objective and accurate determination of disease course is of great significance for the prevention and treatment of rice blast. Mobile phone APP is used as image acquisition port, which does not rely on other instruments and equipment and could obtain accurate grading results of rice blast in real time, reduce the threshold of research and improve the efficiency of scientific research.

Key words: Rice Diseases, Rice Blast, Image Processing, Machine Vision, GrabCut, Disease Course Grading