Abstract: 【Objective】In agricultural pest forecasting, it is often necessary to identify several important pests from a large number of insects. At present, most of the researches on agricultural pest identification are based on limited pest species and limited sample sizes. In order to identify nine species of rice forecasting pests from a large number of agricultural insect images, a method based on multi-feature and sparse representation for pest image identification was proposed in this paper. 【Method】 Firstly, in order to obtain an optimal identification model of agricultural pests, all pest images were rotated to make insect head upright,  cropped with 1:2 aspect ratio to make insect center and take up a large portion of the image, and scaled to a uniform size with 48×96 pixels. The HSV color features, LBP features, Gabor features and HOG (histogram of oriented gradient) features of each image were extracted. Then, the overcomplete dictionaries based on single features or multi-feature were constructed and each column vector represented a training sample. The same training sample species were in the same subspace. a testing sample was sparsely represented by an overcomplete dictionary and a sparse solution was obtained by solving the optimization of the l₁ norm to make different training sample species with zero or near-zero coefficient. Finally, the threshold value of sparse concentration index was used to determine the validity of the testing sample. If the sparse concentration index of a testing sample was greater than the threshold value, the pest was identified as the species with the minimizing reconstruction error. Otherwise, the testing sample was judged to be a non-forecasting pest. The support vector machine (SVM) classifiers were trained on the same features and training samples to compare with the sparse representation identification model of agricultural pests. 【Result】 In the sparse representation identification models trained on single feature, the model based on HOG features could get the higher identification rate of 87.0% and lower false detection rate of 7.5% in the nine rice forecasting pests. The sparse representation identification model based on color and HOG features obtained the highest identification rate of 90.1% and lowest false detection rate of 5.2%. The identification rate decreased and the false detection rate rose in the sparse representation models based on color, HOG and Gabor features. The identification rates of the SVM classifiers were lower and the false detection rates were higher than the sparse representation models trained on the same features. 【Conclusion】 The sparse representation pest identification model based on color and HOG features obtained the higher identification rate and lower false detection rate of agricultural forecasting pests. The threshold value of sparse concentration index could effectively exclude the non-forecasting insects. The sparse representation pest identification model could automatically identify the forecasting pests from a large number of insects.

Key words: agricultural forecasting pest, feature fusion, sparse representation, identification model, support vector machine