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Pest image identification method based on multi-space convolution neural network

A convolutional neural network and image recognition technology, applied in the field of image recognition, can solve the problems of low pest image recognition rate and poor robustness, and achieve the effect of enhancing robustness, improving accuracy and excellent feature expression ability.

Active Publication Date: 2017-06-13
HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI
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Problems solved by technology

[0004] The purpose of the present invention is to solve the defects of low pest image recognition rate and poor robustness in the prior art, and provide a pest image recognition method based on multi-space convolutional neural network to solve the above problems

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[0044] In order to have a further understanding and understanding of the structural features of the present invention and the achieved effects, the preferred embodiments and accompanying drawings are used for a detailed description, as follows:

[0045] like figure 1 As shown, a kind of pest image recognition method based on multi-space convolutional neural network of the present invention comprises the following steps:

[0046] In the first step, the training images are collected and preprocessed. Collect several images as training images, where the training images have category labels, and carry out size normalization processing on all training images according to the prior art method, and process them into 256×256 pixels to obtain several training samples, and use for supervised deep network training.

[0047] The second step is to construct a multi-scale MS-CNN network model and a multi-kernel classification model. Take the training sample as input, complete the trainin...

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Abstract

The invention relates to a pest image identification method based on a multi-space convolution neural network, and solves the shortcomings of low image identification rate and poor robustness compared with the prior art. The method comprises the following steps of collecting and pre-processing training images, constructing a multi-scale MS-CNN network model and a multi-core classification model, collecting and pre-processing image to be tested, taking a test sample as input and training the multi-core model and the MS-CNN model, and conducting automatic identification of pest images after the training. According to the pest image identification method based on the multi-space convolution neural network, the accuracy rate of pest identification is improved, the robustness of a pest identification algorithm is enhanced and the practical application level is achieved.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to a pest image recognition method based on a multi-space convolutional neural 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 kinds of pests, and every plant protection expert can only identify some of them. There are growing signs that the growing need for pest identification is at odds with the relatively small number of pest identification experts. Today, in the field of pattern recognition, the unsupervised deep learning theory has become a research hotspot for many scholars, and it has been widely used in the fields of face recognition and object recognition, and has achieved go...

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Application Information

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IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/08G06V20/20G06N3/045
Inventor 陈红波谢成军张洁李瑞余健陈天娇王儒敬宋良图
Owner HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI
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