Cotton pest identification and classification method and device

Abstract

The embodiment of the invention provides a method and a device for identifying and classifying cotton pests. The method obtains an original image containing cotton pests to be classified, and obtainsHu invariant moment parameters of the original image and overall contour characteristic parameters of cotton pests to be classified. Based on Otsu threshold segmentation algorithm and Canny edge detection algorithm, the image of cotton pests to be classified is separated from the background of the original image, the wing image is extracted from the original image, and the wing contour characteristic parameters of cotton pests to be classified are obtained according to the wing image. Mathematical morphology algorithm is used to optimize the wing image and extract the corresponding mathematical morphological parameters of the wings of cotton pests to be classified. Radial basis function neural network is used to classify cotton pests. The method and the device enable the identification andclassification of cotton pests to be more accurate, thereby playing an important role in the targeted control of cotton pests and reducing the economic losses caused by cotton pests.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of digital image processing, and more specifically, to methods and devices for identifying and classifying cotton pests. Background technique [0002] Cotton is an important economic crop. With the rapid development of the cotton industry, new breakthroughs have been made in cotton science and technology; however, the technology of cotton pest control has not improved much, resulting in huge losses caused by cotton pests and diseases. There are many types and large numbers of pests in cotton fields. The backward identification method of cotton pests is an important reason for the slow development of cotton pest control technology. Moreover, the current identification and classification methods for cotton pests are single, and there is a lack of scientific, reasonable and accurate classification methods for cotton pests. [0003] The prior art provides a method for identifying and classi...

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Problems solved by technology

However, due to the fuzzy clustering algorithm used in the above scheme, this method needs to know the number of categories in advance. If the number of categories is unreasonable, it needs to be recalculated, resulting in an excessive amount of calculation.

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