Image database establishing method suitable for training deep convolution neural network

A construction method and neural network technology, applied in the field of precision agriculture, can solve problems such as insufficient attitude and angle range, insufficient generalization ability of model classification, overfitting and other problems

Active Publication Date: 2015-09-09
ZHEJIANG UNIV
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AI Technical Summary

Problems solved by technology

At present, the number of samples of pest image classification models for a certain pest or all pests of a certain crop is too small, and the range of poses and angles co

Method used

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  • Image database establishing method suitable for training deep convolution neural network
  • Image database establishing method suitable for training deep convolution neural network
  • Image database establishing method suitable for training deep convolution neural network

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Embodiment Construction

[0041] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be noted that the following embodiments are intended to facilitate the understanding of the present invention, but do not limit it in any way.

[0042] Process flow of the present invention such as figure 1 As shown, it mainly includes the following steps:

[0043] Embodiments Taking the original image shown in FIG. 2(a) as an example, the method for processing the original image in the present invention will be described in detail.

[0044] step one:

[0045] The images of 30 typical rice pests were retrieved from the Internet, the samples with small size were manually screened, and the qualified samples were marked by experts to the pest category.

[0046] The selected 30 kinds of rice pests include: rice borer, rice borer, rice leaf roller, rice bract moth, diamondback moth, Indian rice borer, corn borer, brown planthopper, brown pl...

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Abstract

The invention discloses an image database establishing method suitable for training a deep convolution neural network, and the method comprises the following steps: collecting an original image in a pest RGB form, and recognizing and marking pest categories; performing color attenuation on the original image to obtain an RGB image and a corresponding HSV image; calculating respective color proportions according to the RGB image, calculating color similarity regions according to the HSV image, and calculating a significance image of the RGB image; thresholding the significance image to obtain a binary image, using a region appointed by the binary image as an initial region, and dividing a pest target in the initial region with a GrabCut algorithm; cutting the original image according to the divided region, zooming to a uniform size and storing and establishing a database. The problems of difficulty in sampling, fuzzy marking and non-uniform size when a crop pest database is established in the prior art are solved, and the problem of over-fitting caused by too less samples and single gestures in a training process of the deep convolution neural network is solved.

Description

technical field [0001] The invention relates to the technical field of precision agriculture, in particular to a method for constructing an image database suitable for training a deep convolutional neural network. Background technique [0002] Rice is one of the important food crops in my country. During the whole growth period of rice, there are many diseases, insects and other harmful organisms, especially rice pests, which cause staggering losses every year, directly endangering rice production. At present, my country's rice pest control has always adhered to the "Integrated Pest Management (IPM)" plant protection policy, based on monitoring and forecasting, comprehensively applying agricultural, biological, physical control and chemical control and other technical measures to effectively control pest damage. [0003] The investigation of the types and quantities of rice pests is a basic and important task in the pest forecasting work. If there is no correct survey data,...

Claims

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/51
Inventor 何勇刘子毅杨国国
Owner ZHEJIANG UNIV
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