Leaf Disease Detection on Cucumber Leaves Using Multiclass Support Vector Machine

被引:0
作者
Krithika, P. [1 ]
Veni, S. [1 ]
机构
[1] Amrita Univ, Amrita Vishwa Vidyapeetham, Amrita Sch Engn, Dept Elect & Commun Engn, Coimbatore 641112, Tamil Nadu, India
来源
2017 2ND IEEE INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, SIGNAL PROCESSING AND NETWORKING (WISPNET) | 2017年
关键词
k-means clustering; Multiclass SVM; Image processing;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In India, smart organic farming is gaining importance. There may be problems due to environment, temperature, humidity or nutrient deficiency in this farming. If we have a monitoring system for this farming it is possible to produce healthy plant. The aim is to address this issue using computer aided image processing technique. Main solution is to create an automation system which can detect the disease present in the leaf of the plant. In this paper, a first level attempt is made to detect diseases present in the leaf of salad cucumber. The most common diseases which are present in salad cucumber are Alternaria leaf blight, Bacterial wilt, Cucumber green mottle mosaic, Leaf Miner, Leaf spot, Cucumber Mosaic Virus (CMV) disease and so on. K-means clustering, an unsupervised algorithm along with Support Vector Machine(SVM) is used in this work to address this problem.
引用
收藏
页码:1276 / 1281
页数:6
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