Farmland Weed Species Identification Based on Computer Vision

文献类型： 外文期刊

第一作者： Liu, Shengping

作者： Liu, Shengping;Li, Zhemin;Zhong, Xiaochun;Wang, Junchan;Tao, Liu;Sun, Chengming

作者机构：

关键词： Weed communities; Index extraction; Image processing; Class; Computer vision

期刊名称：COMPUTER AND COMPUTING TECHNOLOGIES IN AGRICULTURE XI, PT I

ISSN： 1868-4238

年卷期： 2019 年 545 卷

页码：

收录情况： SCI

摘要： In order to alleviate the difficulties in collecting indexes for the analysis of farmland weed communities, we implemented a computer vision technology-based method for the identification of farmland weeds at the species level. By using the super-green and maximum interclass difference methods to obtain a green vegetation binary image, we were able to separate weeds from cultivated crops through multiple etching and the removal of small areas. A BP (back propagation) neural network was used for weed recognition, and the morphological characteristics of the weeds and each region were selected following etching to construct the input matrix of the recognition model for training and testing the BP network. After experimenting with the computational vision method for the identification of five weed species, we discovered that the recognition accuracy rate reached 96%. The results showed that the computer vision method could quickly and accurately extract a weed community analysis index, thereby providing a reference for the intelligent analysis of weed communities.

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