Rice pest intelligent recognition and classification system

Abstract

The invention discloses a rice pest intelligent recognition and classification method based on principal component analysis. According to the method, discrete sine transform (DST) and modular 2-dimension principal component analysis (modular 2D PCA) are combined to achieve rice pest image intelligent recognition. The method includes the steps that firstly, rice pest images are subjected to the DST and then are compressed, IDST is performed on the images to reestablish images to filter out high-frequency parts insensitive to human eyes; secondly, rice pest feature extraction and mode recognition are performed through the modular 2D PCA method. Accordingly, the method plays an important role in reducing feature dimensions and maintaining illumination, colors, gestures and other insensitive classification features. According to the rice pest intelligent recognition and classification system, images shot by digital cameras can be applied to computer programs to be processed, unknown pest images can be automatically compared with training sample database pest images in a data storer through Matlab software, and then recognition results can be output to a display.

Description

technical field [0001] The invention relates to the fields of insect taxonomy and computer image intelligent recognition, in particular to the intelligent recognition and classification technology of rice pests. Background technique [0002] The identification of rice pests is an important basis for the forecasting of rice pests and diseases, which is directly related to the integrated pest management and the improvement of rice yield and quality. At present, the forecasting and forecasting of field rice pests in my country is mainly carried out by agricultural protection professionals through traditional insect classification methods, such as species descriptions, biological characteristic maps and retrieval tables, etc. The workload is heavy and the efficiency is low, and it is difficult to meet other disciplines and production practices. And the general public's demand for accurate, rapid and timely identification of rice pest species lags behind the urgent demand for time...

AI Technical Summary

Problems solved by technology

At present, the forecasting and forecasting of field rice pests in my country is mainly carried out by agricultural protection professionals through traditional insect classification methods, such as species descriptions, biological characteristic maps and retrieval tables, etc. The workload is heavy and the efficiency is low, and it is difficult to meet other disciplines and production practices. And the general public's demand for accurate, rapid and timely identification of rice pest species lags behind the urgent demand for timeliness and popularity of forecasting information in production, and the intelligent identification technology of rice pests needs to be developed urgently

Before this study, no one has systematically combined computer image intelligent recognition technology with rice pest taxonomy to research and develop rice pest intelligent recognition and classification system

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