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Pest image detection method based on crop identification cascade technology

A technology of image detection and pests, which is applied in character and pattern recognition, biological neural network models, instruments, etc., can solve the problems that the intercorrelation of pests affects the detection results of pests, and achieve the effect of improving effectiveness

Active Publication Date: 2020-05-19
HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI
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Problems solved by technology

[0005] The purpose of the present invention is to solve the defect that the cross-correlation between different types of pests affects the detection results of pests in the prior art, and to provide a method for detecting pest images based on crop recognition cascading technology to solve the above problems

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  • Pest image detection method based on crop identification cascade technology
  • Pest image detection method based on crop identification cascade technology
  • Pest image detection method based on crop identification cascade technology

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Embodiment Construction

[0043] 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:

[0044] By collecting and analyzing our data set, we found that some pests will appear on specific crops, and have different time, space, and various environmental information such as temperature and other sensory information. It is very useful to introduce these sensory information into the detection. Necessary, our proposed method is to first classify crops in pest images according to multiple perceptual information, and then train corresponding pest detection models for different crops.

[0045] In addition, we observed that most of the pests on the images are position-independent and small in size, and using the most advanced deep learning object detection methods in these images, it is easy to lose the pest position information aft...

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Abstract

The invention relates to a pest image detection method based on a crop identification cascade technology. Compared with the prior art, the defect that pest detection results are influenced by cross correlation among different types of pests is overcome. The method comprises the following steps: acquiring a basic data image; constructing and training a multi-layer perception information identification network; constructing and training a multi-projection detection model; acquiring to-be-detected pest images; detecting the pest images. The invention provides a two-stage cascade pest detection method based on mobile vision, the method can be applied to large-scale various pest data, the method is assessed in a newly established large-scale data set grain crop field pest data set, and sufficient experimental results show that the method is superior to a traditional advanced object detection method.

Description

technical field [0001] The invention relates to the technical field of pest image detection, in particular to a pest image detection method based on crop identification cascade technology. Background technique [0002] In the existing technology, the use of mobile vision technology for automatic wild pest detection and identification is a hot topic in modern intelligent agriculture, but it also faces severe challenges, including the complexity of the wild environment, the detection of tiny pests and the classification of multiple pests . Although recent deep learning-based mobile vision techniques have achieved some success in overcoming the aforementioned problems, a key issue is that for large-scale multi-pest data, unbalanced classes significantly reduce their detection and identification accuracy. [0003] Generally speaking, the traditional deep learning pest image recognition method is trained for a large number of pest images, involving multiple pest species, such as...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06V20/46G06N3/045G06F18/214G06F18/241Y02A40/10
Inventor 陈天娇王儒敬王方元谢成军刘万才张洁李瑞陈红波董伟胡海瀛
Owner HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI
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