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Farmland pest recognition method based on colors and fuzzy clustering algorithm

A technology of fuzzy clustering and recognition methods, applied in character and pattern recognition, computing, computer parts and other directions, can solve problems such as high labor intensity, large subjective factors, and low efficiency

Inactive Publication Date: 2013-04-10
SICHUAN AGRI UNIV +1
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  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

At present, black light trapping and manual identification are widely used to count the types and densities of pests. This method is labor-intensive, low in efficiency, and subjective factors are relatively large, which affects the accuracy and timeliness of forecasting.

Method used

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  • Farmland pest recognition method based on colors and fuzzy clustering algorithm
  • Farmland pest recognition method based on colors and fuzzy clustering algorithm
  • Farmland pest recognition method based on colors and fuzzy clustering algorithm

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Embodiment Construction

[0050] The process of identifying farmland pests will be specifically described below in conjunction with the accompanying drawings.

[0051] The original image format of farmland pests is in RGB format, but there is a strong correlation between the three components of R, G, and B. As the lighting conditions change, the three components of R, G, and B will change greatly. These components often cannot get the desired effect, so in the selection of the image color space, the HSI space is selected.

[0052] In the HSI color space conversion of the image, the color information of the image is mainly reflected by H and S. The conversion formula from RGB to HSI space is as follows:

[0053] H = 2 π - θ B > G θ ...

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Abstract

The invention relates to the fields of agriculture and image processing technology and discloses a farmland pest recognition method based on colors and the fuzzy clustering algorithm. According to the farmland pest recognition method, on one hand, acquired farmland pest original images are converted from colored RGB (red, green, blue) images into HSI color spaces, image saturation enhancing calculation is conducted, and color pest characteristic parameters are collected; on the other hand, the colored RGB images of farmland pests are converted into grayscale images, self-adaptive binarization processing is conducted so that binarization images are obtained, and then shape characteristic parameters such as the area and circumference are collected; and finally the fuzzy clustering algorithm is conducted on all the parameters, and a radial basis function neural network is combined to finish identification of six common farmland pests. The farmland pest recognition method combines the color characteristic parameters of the farmland pests and the fuzzy clustering algorithm, and greatly improves the pest identification accuracy rate. Verified by tests, the identification accuracy rate can reach 95.1%.

Description

technical field [0001] The invention belongs to the technical fields of agriculture and image processing, and relates to a method for identifying farmland pests based on color and shape features combined with fuzzy clustering algorithms and RBF radial basis function neural networks. Background technique [0002] my country is a large agricultural country, and agricultural pests occur from time to time, so the monitoring of farmland pests and the statistical forecasting of pest disasters are very important. If the monitoring and forecasting is accurate and timely, pests can be eliminated early and the amount of pesticides can be reduced. At present, black light trapping and manual identification are widely used to count the species and density of pests. This method is labor-intensive, low in efficiency, and subjective factors are relatively large, which affects the accuracy and timeliness of forecasting. Therefore, the real-time and accurate identification of farmland pests ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
Inventor 汪建
Owner SICHUAN AGRI UNIV
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