A rice field weed identification method based on GA-ANN feature dimension reduction and SOM feature optimization

A technology of feature dimensionality reduction and recognition method, which is applied in the direction of character and pattern recognition, instruments, computer parts, etc., can solve the problems of limited light adaptability, model adaptability, recognition accuracy to be improved, etc., to improve weed Effects of recognition accuracy, reduction of data redundancy, and improvement of classification accuracy

Inactive Publication Date: 2019-06-25
SOUTH CHINA AGRI UNIV
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Problems solved by technology

[0003] According to the literature review, most recognition algorithms are artificially set thresholds to process images, which have limited adaptability to light and cannot effectively reduce the impact of light intensity; most of them use feature fusion for dimensionality reduction, which reduces the complexity of classification and recognition, but it is difficult to analyze The significance of the influence of feature parameters on different types of weeds; for example, Li Xianfeng et al. proposed a multi-feature fusion weed identification method based on SVM and D-S evidence theory, which solved the problem of feature space

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  • A rice field weed identification method based on GA-ANN feature dimension reduction and SOM feature optimization
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  • A rice field weed identification method based on GA-ANN feature dimension reduction and SOM feature optimization

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Embodiment approach

[0046] Step 1, image collection, under different natural conditions, the digital camera shoots images of paddy field weeds such as Aylophyllum japonicus, Polygonum syringae, Channa saustriae, Arthia sativa, barnyardgrass, and daughter of a daughter, with a resolution of 640×480 pixels. The shooting distance is 50cm from the ground.

[0047] Step 2, image preprocessing, effectively suppress the impact of illumination changes on segmentation accuracy, improve the traditional fixed-parameter color feature factor combination segmentation operator |G-B|+|G-R|, and introduce the weighting coefficient as follows (1), the operator is set by weighting The factor value can be used to segment and process field images under different light conditions to obtain binary images, and then use morphological operators to perform post-processing and separate weeds to obtain binary images of weeds.

[0048] I Gray =ε|G-B|+(1-ε)|G-R| (1)

[0049] In the formula, I Gray is the gray value of the p...

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Abstract

The invention discloses a rice field weed identification method based on GA-ANN feature dimension reduction and SOM feature optimization. The method comprises the following steps: step 1, image acquisition; Collecting images of rice field weeds as samples under different natural conditions; Step 2, image preprocessing; In order to solve the problem that the weed identification precision is influenced by paddy field illumination intensity change and water surface reflection, on the basis of an RGB color feature segmentation algorithm, introducing an RGB linear weighting coefficient to segment an image to obtain a binary image, and then using a morphological operator for post-processing to separate out a weed binary image; According to the method, data redundancy can be reduced, the calculation amount is simplified, the screened main characteristics have the characteristics of independence, distinguishability and small number, the method can be effectively applied to weed classificationand recognition, and the classification precision is improved.

Description

technical field [0001] The invention relates to the field of weed identification in paddy fields, in particular to a method for identifying weeds in paddy fields based on GA-ANN feature dimensionality reduction and SOM feature optimization. Background technique [0002] Rice is one of the main food crops in my country, and weeds in rice fields are an important factor affecting the yield and quality of rice. Effective identification and accurate classification of weeds is a crucial step in realizing intelligent and precise weeding. Due to the characteristics of paddy fields, there are problems such as changes in light intensity and reflections on the water surface that have a great impact on the accuracy of weed recognition. In order to improve the accuracy of different weed recognition, it is necessary to analyze various characteristics of weeds (shape features, texture features, color features) Fusion application, but with the increase of the feature dimension, the recogni...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06K9/46
Inventor 陈学深方贵进陈林涛马旭齐龙陈涛黄柱健
Owner SOUTH CHINA AGRI UNIV
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