Pest image classification method based on context sensing dictionary learning

Abstract

The invention provides a pest image classification method based on context sensing dictionary learning. The method comprises the following steps that: context sensing information of pest images in the known category is added into a pest image sample base to obtain a plurality of types of training samples, a learning function is constructed, and the training samples are used for completing pest image redundant dictionary learning; the pest images to be classified are subjected to preprocessing to obtain test samples; the test samples are subjected to sparse representation dimensionality reduction processing; the test samples subjected to the sparse representation dimensionality reduction processing are read into a sparse representation classifier, and the residual error of the context sensing information of the test samples and various types of the training samples is calculated according to a redundant dictionary obtained through learning; and the residual error of the context sensing information of the test samples and various types of the training samples is analyzed, and the categories of the test samples are determined. The pest image classification method has the advantages that the precision and the efficiency of the pest image classification in complicated scenes can be improved, and a traditional crop pest diagnosis mode is improved.

Description

technical field [0001] The invention relates to technical fields such as computer vision, pattern recognition, and intelligent agriculture, and in particular relates to a pest image classification method based on context-aware dictionary learning. Background technique [0002] Pests are the enemies of crops, and they occur throughout the growth period of crops, which can cause a large reduction in crop yield. Although the application of chemical pesticides can greatly reduce agricultural losses, the "three causes" caused by the use of chemical pesticides (referring to the mutagenic, carcinogenic, and teratogenic effects of pesticides on higher animals), pesticide residues, and environmental pollution have become increasingly prominent. It can be seen that it is particularly important to predict and forecast pests for effective control, but one of the prerequisites for these tasks is to accurately classify and identify pests. The traditional pest image classification method ...

AI Technical Summary

Problems solved by technology

The traditional pest image classification method has excellent performance under the premise that the environment is effectively controlled. However, in the real scene, for the complex background of the pest image, the different postures of the pest in the image, etc., only the appearance feature information of the pest image is used for training. It is difficult to identify and the accuracy of identification is limited

Images