Agricultural pest image recognition method based on multi-feature deep learning technology

A deep learning and image recognition technology, applied in the field of image processing, can solve problems such as poor image recognition performance of pests

Active Publication Date: 2016-04-13
HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI
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AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to solve the defect of poor pest image recognition performance under complex environmental conditions

Method used

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  • Agricultural pest image recognition method based on multi-feature deep learning technology
  • Agricultural pest image recognition method based on multi-feature deep learning technology
  • Agricultural pest image recognition method based on multi-feature deep learning technology

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Embodiment Construction

[0075] 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:

[0076] This method first obtains large-scale pest sample images, which only include individual pests and do not include the expression of the external environment. According to the pest samples, different pests are distinguished from the five features of color feature, texture feature, shape feature, scale invariant feature transformation feature and direction gradient histogram feature. The multi-feature representation-multi-feature encoding histogram is obtained by deep learning for five features, and then the multi-core learning classifier is used to classify pest images (including pest images in the external environment), thereby improving the recognition of pest images.

[0077] Such as figure 1 As shown, a method for image reco...

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Abstract

The invention relates to an agricultural pest image recognition method based on a multi-feature deep learning technology. In comparison with the prior art, a defect of poor pest image recognition performance under the complex environment condition is solved. The method comprises the following steps of carrying out multi-feature extraction on large-scale pest image samples and extracting color features, texture features, shape features, scale-invariant feature conversion features and directional gradient histogram features of the large-scale pest image samples; carrying out multi-feature deep learning and respectively carrying out unsupervised dictionary training on different types of features to obtain sparse representation of the different types of features; carrying out multi-feature representation on training samples and constructing a multi-feature representation form-multi-feature sparse coding histogram for the pest image samples through combining different types of features; and constructing a multi-core learning classifier and constructing a multi-core classifier through learning a sparse coding histogram for positive and negative pest image samples to classify pest images. According to the method, the accuracy for pest recognition is improved.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an image recognition method for farmland pests based on multi-feature deep learning technology. Background technique [0002] Relying on scientific and technological innovation to promote agricultural development has become the theme of agricultural development today. Computer technology plays a very important role in emerging agricultural science and technology, and pest image recognition technology is one of them. Using this technology can timely and accurately identify pests, reduce the use of pesticides, improve crop yield and quality, and protect the ecological environment. Nowadays, a variety of pest image recognition methods have been proposed, and a certain recognition accuracy has been achieved under the premise that the environment is effectively controlled. However, in the actual complex farmland environment, the pest image is disturbed by the background envi...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/24G06F18/2411
Inventor 谢成军宋良图张超凡李瑞张洁周林立陈红波刘磊
Owner HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI
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