Agricultural pest detection method based on regional convolution neural network

A convolutional neural network and agricultural pest technology, applied in neural learning methods, biological neural network models, neural architectures, etc., to achieve real-time pest detection, high accuracy, and easy operation

Active Publication Date: 2018-02-06
CHONGQING UNIV OF POSTS & TELECOMM
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AI Technical Summary

Problems solved by technology

This method solves the problem of effective detection of agricultural pests, saves economic costs and time costs, and can accurately and quickly predict agricultural pests

Method used

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  • Agricultural pest detection method based on regional convolution neural network
  • Agricultural pest detection method based on regional convolution neural network
  • Agricultural pest detection method based on regional convolution neural network

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Embodiment Construction

[0044]The technical solutions in the embodiments of the present invention will be described clearly and in detail below with reference to the drawings in the embodiments of the present invention. The described embodiments are only some of the embodiments of the invention.

[0045] The technical scheme that the present invention solves the problems of the technologies described above is:

[0046] The technical scheme of the present invention is described in detail below in conjunction with accompanying drawing:

[0047] System flow chart such as figure 1 As shown, an agricultural pest detection method based on regional convolutional neural network, including the following steps:

[0048] The first step: collect and organize various types of agricultural pest samples as a training set;

[0049] The second step: mark the sample label operation on the training set to perform model training on the regional convolutional neural network;

[0050] The third step: use the regional ...

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Abstract

The invention provides an agricultural pest detection method based on a regional convolution neural network and relates to the technical fields of digital image processing, deep learning, computer vision and the like. The method comprises the following specific steps of 1) collecting and arranging various agricultural pest samples as a training set; 2) marking and labeling samples in the trainingset; 3) subjecting all collected samples in the agricultural pest training set to model training by means of a regional convolution neural network framework; 4) collecting a monitoring image in the farmland, and testing the monitoring image to obtain a final effect based on the obtained model. According to the method, agricultural pests are detected by the obtained model based on the regional convolution neural network, and the influence of agricultural pests on the agriculture, the forestry and the animal husbandry is reduced with the assistance of various pest prevention means. The yield ofagricultural grains is improved, and the greening safety of the forestry and the animal husbandry is protected. The method has a practical significance and is good in effect.

Description

technical field [0001] The invention relates to an agricultural pest detection method based on a regional convolutional neural network, and belongs to the technical fields of digital image processing, deep learning, machine vision and the like. Background technique [0002] Images are commonly used information carriers in human society. Studies have shown that the visual image information acquired by humans accounts for nearly 80% of the information received by humans. It can be seen that visual information is important to human beings, and images are the main way for human beings to obtain visual information. Since the 1960s, with the continuous improvement and popularization of computer technology, digital image processing has developed rapidly at home and abroad, and is widely used in scientific research, industrial and agricultural production, biomedical engineering, aerospace, military, industry, robot industry, etc. Fields are playing an increasingly important role i...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06T7/00G06T7/73G06N3/04G06N3/08
CPCG06T7/0012G06T7/73G06N3/084G06T2207/10004G06T2207/20076G06T2207/20084G06T2207/20081G06T2207/30004G06N3/045G06F18/2413G06F18/24147
Inventor 肖斌魏杨李伟生
Owner CHONGQING UNIV OF POSTS & TELECOMM
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