Multi-Feature Fusion in Weed Recognition Based on Dempster-Shafers Theory
As accurate identification of weeds from crops is the prerequisite for precise herbicides spraying, this paper proposes a multi-feature fusion method based on neutral network and D-S evidential theory to improve the accuracy of weed recognition. Firstly, three kinds of single features such as color, shape and texture are extracted from the weed and crop leaves after a series of image processing. Secondly, the leaves are classified with each kind of feature by neutral network and the output of each sub-network are made as an independent evidences to construct the basic belief assignment. Finally, using D-S combination rule of evidence to achieve the decision and giving final recognition results by classification rules. The experimental results have shown that the multi-feature fusion method has good performance on accuracy compared to the single feature-based method in weed recognition.
feature extraction neural network D-S theory features fusion weed recognition
Xianfeng Li Weixing Zhu
School of Electrical & Information Engineering Jiangsu University Zhenjiang, P.R.China
国际会议
太原
英文
127-130
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）