Research on Cucumber Downy Mildew Detection System based on SVM Classification Algorithm

DOI

10.2991/ic3me-15.2015.324How to use a DOI?

Keywords

cucumber downy mildew; support vector machine; feature extraction; detection

Abstract

Cucumber, a common economic crop, occupies a large proportion of vegetable cultivation in China. Plant diseases and insect pests, especially the cucumber downy mildew, are important causes for the decrease in the yield of cucumbers. In order to reduce the losses caused by pests and diseases and achieve rapid automatic identification of plant diseases and insect pests, this paper studies machine vision system and disease image detection with support vector machine (SVM) classification algorithm, taking cucumber downy mildew for example. This paper carries out a method study in image acquisition, image preprocessing, feature parameter extraction, and pattern recognition, which obtains satisfactory results. The accuracy of cucumber downy mildew detection reaches 90%, significantly higher than that of artificial recognition.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Bingyu Zhou
AU  - Jingwen Xu
AU  - Junfang Zhao
AU  - Aiwen Li
AU  - Qiuyu Xia
PY  - 2015/08
DA  - 2015/08
TI  - Research on Cucumber Downy Mildew Detection System based on SVM Classification Algorithm
BT  - Proceedings of the 3rd International Conference on Material, Mechanical and Manufacturing Engineering
PB  - Atlantis Press
SP  - 1681
EP  - 1684
SN  - 2352-5401
UR  - https://doi.org/10.2991/ic3me-15.2015.324
DO  - 10.2991/ic3me-15.2015.324
ID  - Zhou2015/08
ER  -