Classification of agricultural pests based on compressed sensing theory

In order to improve the effectiveness of the existing classification methods of pests, a novel classification method of pests was presented by using compressed sensing theory. In the proposed method, a large number of the representative training samples of pests were used to construct the training samples matrix, and then the sparse decomposition representation of the testing samples of pests was obtained by solving the l1-norm optimization problem, which had distinct class information and could be used for the different species of pest classification directly. The 12 species of stored-grain pests and the 110 species of common pests were separately classified by the proposed method, and the classification precision reached around 92.9418%、98.2877%、78.8651% and 61.5938% respectively under 4 kinds of different experimental conditions. The experimental results indicated that the application of compressed sensing theory in the classification of pests was practical and feasible.