Injurious insect image automatic recognition method and system based on deep restricted Boltzmann machine

Abstract

The invention relates to an injurious insect image automatic recognition method based on a deep restricted Boltzmann machine. The method comprises a training process and a testing process. According to the training process, data of a training image set are preprocessed, preprocessed training images are grouped to construct a training image cube, features of each training image group are extracted through a restricted Boltzmann machine algorithm, and trained training image set feature data are obtained through feedback adjustment. According to the testing process, a test image to be recognized is input and is preprocessed, features of the test image are extracted through the restricted Boltzmann machine algorithm, and feature data of the test image with small errors are obtained through feedback adjustment; the varieties of injurious insects are recognized, and a preventive method is given out. The invention discloses an injurious insect image automatic recognition system based on the deep restricted Boltzmann machine. The injurious insect recognition rate and procedure robustness are improved, and the actual application value of injurious insect recognition in agricultural production is improved.

Description

technical field [0001] The invention relates to the technical fields of intelligent agriculture and pattern recognition, in particular to a method and system for automatic recognition of pest images based on a depth-restricted Boltzmann machine. Background technique [0002] Pests are the enemy of crop growth, they occur throughout the growth period of crops, and can cause a large amount of crop yield reduction. The current pest classification and identification work is mainly done by a small number of plant protection experts and agricultural technicians. However, there are many types of pests, and every plant protection expert can only identify some pests. There are more and more indications that the contradiction between the increasing demand for pest identification and the relatively few pest identification experts has been intensified. The automatic identification of pest images is of great significance, but the recognition rate of automatic pest image identification me...

AI Technical Summary

Problems solved by technology

The current pest classification and identification work is mainly done by a small number of plant protection experts and agricultural technicians. However, there are many kinds of pests, and every plant protection expert can only identify some pests

There are more and more indications that the contradiction between the increasing demand for pest identification and the relatively few pest identification experts has been intensified. The automatic identification of pest images is of great significance, but the recognition rate of automatic pest image identification methods is low. The robustness is poor, and only exists in the experimental stage. It is of great significance to seek a pest identification method with high recognition rate and strong robustness.

Images