A LDA-based segmentation model for classifying pixels in crop diseased images

Na Wu, Miao Li, Lei Chen, Yuan Yuan, Shide Song

科研成果: 书/报告/会议事项章节会议稿件同行评审

9 引用 (Scopus)

摘要

Effective segmentation of symptoms from crop diseased images is a vital important step in the timely detection of crop disease based on image processing techniques. Many of the formerly proposed methods still did not show a satisfactory performance in the extraction of symptoms from RGB images, especially when the images contain specularly reflected and shadowed parts. In this paper, we propose a novel approach to classify individual pixels in crop diseased images taken in the field as diseased or healthy. The approach is based on the machine learning algorithm linear discriminant analysis (LDA) and color transformation. Five color spaces were applied and compared over diseased images infected by four diseases commonly observed in cucumber crops - target spot, angular leaf spot, downy mildew and powdery mildew. The experimental results demonstrated that our proposed approach under RGB color space outperformed the other three contrast methods particularly for the images including shadowed and specularly reflected parts. Overall, the proposed LDA-based segmentation model can be used to the symptoms segmentation effectively.

源语言英语
主期刊名Proceedings of the 36th Chinese Control Conference, CCC 2017
编辑Tao Liu, Qianchuan Zhao
出版商IEEE Computer Society
11499-11505
页数7
ISBN(电子版)9789881563934
DOI
出版状态已出版 - 7 9月 2017
已对外发布
活动36th Chinese Control Conference, CCC 2017 - Dalian, 中国
期限: 26 7月 201728 7月 2017

出版系列

姓名Chinese Control Conference, CCC
ISSN(印刷版)1934-1768
ISSN(电子版)2161-2927

会议

会议36th Chinese Control Conference, CCC 2017
国家/地区中国
Dalian
时期26/07/1728/07/17

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