A Hybrid Leaf Disease Detection Scheme Using Grayco-Occurance Matrix Support Vector Machine Algorithm
K. Subhadra1, N. Kavitha2
1K. Subhadra, Research Scholar, Nehru Arts and Science College, Coimbatore (Tamil Nadu), India.
2Dr. N. Kavitha, HOD, Associate Professor, Nehru arts and Science College, Coimbatore (Tamil Nadu), India.
Manuscript received on 10 October 2019 | Revised Manuscript received on 19 October 2019 | Manuscript Published on 02 November 2019 | PP: 300-309 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B10480982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1048.0982S1119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: An Indian economy depends upon the agriculture up to 70% approximately. Hence, there is a need to Take care of agriculture and its resources. In such aspects, the plant disease and leaf disease is one of the major concerns that affect the overall processing of producing food, feed, fiber and many other favorite products by the cultivation. It is one of the reasons that disease identification and detection in plant adopts a significant job in agro industry area. Due to this reason, appropriate detection methodology consideration is to be taken here. Most of the research focused more on combining image processing and soft computing algorithms to solve this issue. With this motivation, this research utilize Median filter for noise removal in initial stage. Later, Hue-Saturation-Value is used for pre-processing. Further, Fuzzy C-Means Clustering (FCM) considered for clustering image samples at different iteration. Finally, the research considered a hybrid mechanism by combining Gray Co-Occurrence Matrix and Support Vector Machine. Further, the proposed method results better outcome in terms of efficiency as 87.43% K-nearest neighbor (KNN) classifier, Color Transform and Exponential Spider Monkey Optimization.
Keywords: Leaf Disease Detection, Gray Co-Occurrence Matrix, Support Vector Machine, K-Nearest Neighbor Classifier, and Color Transform, Exponential Spider Monkey Optimization.
Scope of the Article: Algorithm Engineering