Microscopic image detection and recognition method for rice blast fungus spore based on support vector machine

Abstract

The invention discloses a microscopic image detection and recognition method for a rice blast fungus spore based on a support vector machine. The method comprises the steps of (1) image preprocessingincluding image background correction, median filtering processing and image enhancement processing, (2) image segmentation including binarization operation, morphological operation and edge detectionof a preprocessed image and the obtainment of a graph contour of a suspected rice blast fungus spore, and (3) support vector machine detection and identification including the extraction of most representative shape feature parameters and texture feature parameters from the graph contour of a suspected rice blast fungus spore, the training of a support vector machine classifier model with the shape feature parameters and texture feature parameters as input vectors, and the detection and identification of the rice blast fungus spore by using the trained support vector machine classifier model.According to the method, the rapid and accurate identification of the rice blast fungus spore can be achieved, and a technical support can be provided for the early detection and the discrimination of a disease degree of a rice blast disease.

Description

technical field [0001] The invention relates to the technical field of rice blast detection, in particular to a support vector machine-based method for detection and recognition of rice blast fungus spore microscopic images. Background technique [0002] Rice blast caused by Magnaporthe grisea is one of the three major diseases of rice in the world, which seriously affects the yield and quality of rice. Long-term production practice has proved that the early detection of rice blast disease and the judgment of disease degree are the basis and key to the prediction and chemical control of rice blast disease. Because the symptoms of the disease are not obvious in the early stage of the disease, and agricultural producers lack the corresponding knowledge of crop diagnosis, the disease cannot be diagnosed well, and the crop disease is aggravated. At the current stage, the identification and diagnosis of rice blast disease are mainly divided into two aspects: field detection and ...

AI Technical Summary

Problems solved by technology

Because the individual spores are small and difficult to observe, the number of samples is huge, and other types of pathogenic spores mixed in the samples will interfere with the microscope counting, etc., which greatly increase the difficulty of finding the early stage of the disaster.

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