Weed image segmentation method under rape field environment

Abstract

The invention discloses a weed image segmentation method under the rape field environment. Multiple weed/rape RGB image samples are randomly acquired in the rape field; a visual attention model is established, the color characteristics, the brightness characteristics and the direction componential characteristics are extracted, each characteristic graph is acquired and each characteristic channelsaliency map is generated so that a total saliency map is acquired and the area of interest is acquired; the shape characteristics and the texture characteristics of the area of interest are extractedto perform support vector machine classification training so as to acquire the rape area; and the miscellaneous image samples and all the rape area images are fused so as to acquire the final inter-strain weed area distribution information. The area of interest is acquired through fusion of the improved visual attention model with combination of the region growth algorithm, and the whole algorithm process does not require grayscale transformation or threshold segmentation so that the processing link and the computing amount can be reduced; and the segmentation efficiency is further enhanced by extracting the characteristic parameters of the area of interest and support vector machine classification model judgment so that weed image segmentation under the background of the rape field can be realized.

Description

technical field [0001] The invention belongs to the technical field of weed image segmentation, and in particular relates to a weed image segmentation method in a rapeseed field environment. Background technique [0002] Weeds in farmland have a negative impact on crop growth and soil surface temperature and humidity, resulting in reduced farmland yield and reduced soil reusability. At present, the constant spraying of pesticides used to control the growth of weeds has brought about environmental pollution, food safety and other issues. Therefore, it is of great significance to quickly and accurately identify weeds from field images and obtain their distribution to implement precise spraying. [0003] At present, most of the weed identification systems developed at home and abroad can only detect weeds in simple backgrounds, and the identification efficiency of inter-plant weeds in complex backgrounds is low. Most of its detection methods are to first separate the plant are...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to address the deficiencies of the above-mentioned technologies, and provide a weed image segmentation meth

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