Study on Insect Pests Detection Based on Digital Image

Abstract

This paper mainly performs Cascade AdaBoost algorithm based on multi-feature to detect the images of Eurydema dominulus, which will cause harm to crucifer. Firstly, the mixing of HAAR features and LBP features is adopted instead of the single-feature of traditional model, which makes description of images more comprehensively from the angle of the gradient and texture. And then use the best features selected by Gentle AdaBoost algorithm to compose the weak classifier and the strong classifier. And the cascade detector is composed of the trained classifiers of each layer according to a certain screening rate. Experimental results show that the method of detection has the probability of dis-detecting and leak-detecting, but it still has a certain reference value in the field of agricultural plant diseases and insect pests detection for its good robustness.