农业机械学报2011，Vol.42Issue(11)：164-168,163,6.
基于SVM和D-S证据理论的多特征融合杂草识别方法
Method of Multi-feature Fusion Based on SVM and D- S Evidence Theory in Weed Recognition
摘要
Abstract
According to the low accuracy and low stability of the single feature-based method for weed recognition, a multi-feature fusion method based on SVM and D - S evidence theory was proposed. Firstly, three types of visual features such as color, shape and texture were extracted from the plant leaves after a series of image processing. Then, the plants were classified according to each type of features utilizing SVM and the results were used as evidences to construct the basic probability assignment ( BPA ) . Finally, using D - S combination rule of evidence to achieve the decision fusion and giving final recognition results by classification thresholds. The experimental results show that the accuracy of multi-feature fusion method is over 97% and it has good performance on accuracy and stability compared to the single feature-based method in weed recognition.关键词
杂草识别/特征提取/支持向量机/D-S证据理论/决策级融合Key words
Weed recognition/ Feature extraction/ Support vector machine/ D - S evidence theory/Decision fusion分类
信息技术与安全科学引用本文复制引用
李先锋,朱伟兴,孔令东,花小朋..基于SVM和D-S证据理论的多特征融合杂草识别方法[J].农业机械学报,2011,42(11):164-168,163,6.基金项目
盐城工学院重点建设学科开放基金资助项目(XKY2010021)和江苏大学现代农业装备与技术省部共建教育部重点实验室开放基金资助项目(NZ200709) （XKY2010021）
