文章基本信息

标题：Color Segmentation Scheme for Classifying Weeds from Sugar Beet Using Machine Vision
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作者：A. Jafari ; H. Eghbali Jahromi ; S.S. Mohtasebi 等
期刊名称：International Journal of Information Science and Management (IJISM)
印刷版ISSN：2008-8302
电子版ISSN：2008-8310
出版年度：2012
卷号：4
期号：1
页码：1-12
语种：English
出版社：REGIONAL INFORMATION CENTER FOR SCIENCE AND TECHNOLOGY
其他摘要：In recent years, machine vision and optical sensors, which can be used in an autonomous weed killing equipment, are being used extensively to detect weeds from crops. In this study, seven types of weeds that grow in most of the sugar beet fields in Iran, especially in Fars province, were considered in real outdoor conditions. Several color feature extraction algorithms have also been investigated to separate soil from the plants as well as weeds from the sugar beets. The performance of the proposed algorithm was evaluated by determining correct classification rates (CCR) and misclassification rates (MCR) of the results. The findings revealed that the proposed method could successfully detect five of the seven types of the weeds, including Chenopudium album L., Amaranthus retrofelexus L., Physalis alkekengi L. , Convolvulus arvensi s L., Setaria vertidis L. Beauv and Echinochloa crus-gali (L.) Beauv.