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Leaf Disease Detection on Cucumber Leaves using Multiclass Support Vector Machine

Publication Type : Conference Proceedings

Publisher : Institute of Electrical and Electronics Engineers Inc.

Source : Proceedings of the 2017 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2017, Institute of Electrical and Electronics Engineers Inc., Volume 2018-January, p.1276-1281 (2018)

Url : https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046344076&doi=10.1109%2fWiSPNET.2017.8299969&partnerID=40&md5=f2ff9f5608dc068689dd0b1698e246ba

ISBN : 9781509044412

Keywords : Automation, Clustering algorithms, Cucumber mosaic virus, Image processing, Image processing technique, K - means clustering, Leaf disease detections, Multi-class support vector machines, Multiclass SVM, Nutrient deficiency, Plants (botany), Support vector machines, Unsupervised algorithms, Wireless telecommunication systems

Campus : Coimbatore

School : School of Engineering

Department : Electronics and Communication

Year : 2018

Abstract : 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.

Cite this Research Publication : P. Krithika and S. Veni, “Leaf Disease Detection on Cucumber Leaves using Multiclass Support Vector Machine”, Proceedings of the 2017 International Conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2017, vol. 2018-January. Institute of Electrical and Electronics Engineers Inc., pp. 1276-1281, 2018.

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