Research on Cucumber Downy Mildew Detection System based on SVM Classification Algorithm
- https://doi.org/10.2991/ic3me-15.2015.324How to use a DOI?
- cucumber downy mildew; support vector machine; feature extraction; detection
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.
- © 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/).
Cite this article
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 - https://doi.org/10.2991/ic3me-15.2015.324 ID - Zhou2015/08 ER -