Pest image automatic recognition method based on high-reliability network

A deep belief network and automatic recognition technology, applied in the field of automatic recognition of pest images based on deep belief network, can solve the problems of low recognition rate and poor robustness, and achieve enhanced robustness, improved accuracy, and good classification performance Effect

Inactive Publication Date: 2014-10-01
HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI
View PDF3 Cites 17 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the existing pest image automatic recognition methods and systems have low recognition rate and poor robustness, and only exist in the experimental stage.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Pest image automatic recognition method based on high-reliability network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0023] Below, the present invention will be further described in conjunction with the accompanying drawings and specific embodiments.

[0024] Such as figure 1 As shown, a method for automatic recognition of pest images based on deep belief network, including the following steps:

[0025] S1. Collect several images as training images, preprocess all training images, and obtain several training samples, including the following steps:

[0026] S11. Normalize the size of each training image to 144×144.

[0027] S12. Grayscale the normalized training image, and equalize the grayscale of the grayscaled training image.

[0028] S13. Using a Gaussian filter algorithm to perform smoothing processing on the gray level equalized training image to eliminate the influence of noise on the quality of the training image.

[0029] S2, performing feature extraction on the training samples, including the following steps:

[0030] S21. Using the rectangular orientation gradient histogram HOG...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides a pest image automatic recognition method based on a high-reliability network. The method includes the following steps that preprocessing is carried out on multiple collected training images to obtain multiple training samples, and HOG feature extraction is conducted on the training samples to form joint image feature vectors of the training samples; the high-reliability network based on a restricted boltzmann machine is constructed, the joint image feature vectors of the training samples are input to the constructed high-reliability network, and training on the high-reliability network is completed; preprocessing is conducted on pest images to be tested to obtain test samples, and HOG feature extraction is carried out on the test samples to form joint image feature vectors of the test samples; the joint image feature vectors of the test samples are input to the high-reliability network after training is finished, and categories of past images to be tested are obtained through recognition. According to the method, the accuracy rate of pest recognition can be improved, and robustness of a pest recognition algorithm is enhanced.

Description

technical field [0001] The invention relates to the technical field of intelligent agriculture and pattern recognition, in particular to a method for automatic recognition of pest images based on a deep belief network. 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. The current pest classification and identification work is mainly done by a small number of plant protection experts and agricultural technicians, but there are many types of pests, and every plant protection expert can only identify some pests. There are growing signs that the growing need for pest identification is at odds with the relatively small number of pest identification experts. However, the existing pest image automatic recognition methods and systems have low recognition rate and poor robustness, and only exist in the experimental stage. Therefore, it is of great significance to seek ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00
Inventor 王儒敬洪沛霖谢成军李瑞张洁宋良图董伟周林立郭书普张立平黄河聂余满
Owner HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap