Method for recognizing diseases of crop leaves

Abstract

The invention discloses a method for recognizing diseases of crop leaves. The method comprises the steps of converting acquired images into CIELab colored space images; calculating the saliency of each pixel point according to a multi-scale neighborhood to obtain a final salient image; cutting the final salient image into K areas by the K means clustering method; extracting the area of which the average value of the saliency of the pixel points is more than a set threshold as a salient area; adjusting the extracted salient area to obtain a scab image; extracting the color and local texture feature parameters of the scab image; inputting the obtained color and local texture feature parameters into the neural network for recognizing and classifying the diseases. With the adoption of the method, the extracted scab image is free of color distortion, and the diseases recognition accuracy is high.

Description

technical field [0001] The invention belongs to the technical field of crop cultivation, and in particular relates to a method for identifying crop leaf diseases based on image processing. Background technique [0002] my country has a vast territory, complex and diverse terrain and climate types, which are divided into tropical, subtropical, temperate and frigid zones from south to north. However, our country has a large population and relatively little arable land. Therefore, agriculture, especially planting, plays a very important role in our country, which is related to the entire national economy and people's livelihood. my country's main food crops include rice, wheat, corn, soybeans, etc., and economic crops include cotton, peanuts, rapeseed, sugar cane, and sugar beets. However, with the development of industry, the ecological system in our country is becoming weaker and weaker, and the diseases of crops are very serious. There are many kinds of diseases, they are w...

AI Technical Summary

Problems solved by technology

[0004] However, in the existing crop disease recognition and classification, the disease spot segmentation mainly uses the edge detection method, the maximum inter-class variance method, the fuzzy C-means method and the watershed segmentation method. These methods require complex field background segmentation and a series of preprocessing. , increasing the complexity of lesion segmentation

At the same time, in the feature extraction, the texture features in the existing methods are mainly correlation, energy, entropy, contrast and deficit, etc., which define many texture features based on the statistical method based on the gray level co-occurrence matrix. Compared with global features, local features have significant advantages such as rotation invariance and gray invariance; and the identification and classification of diseases in crop disease identification and classification mainly use neural networks, support vector machines and improved support vector machine methods, although these The method can identify the types of diseases, but the number of types identified by these methods is small, generally only three types of diseases are identified, and these methods require a lot of samples for training, and the recognition rate is not high

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