Segmentation and Classification of Plant Disease using SVM–PSO in Cloud
Raghavendran.S1, P.Kumar2, Silambarasan.K3
1Raghavendran.S , Research Scholar, Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, Tamilnadu, India and Assistant Professor, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology, Chennai.
2P.Kumar, Assistant Professor, Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, Tamilnadu, India.
3Silambarasan.K, Research Scholar, Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, Abishekapatti, Tirunelveli, Tamilnadu, India.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 20, 2019. | Manuscript published on October 30, 2019. | PP: 2242-2247 | Volume-9 Issue-1, October 2019 | Retrieval Number: A9730109119/2019©BEIESP | DOI: 10.35940/ijeat.A9730.109119
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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: Area of agriculture plant disease detection attracts is very important one, main role is diseases detection. To develop the plant diseases detection, it required to identify arrival of the diseases in the leaf and instruction to the agriculturalists. In this proposed work, a leaf disease detection system (LDDS) based on Otsu segment (OS) is developed to identify and classify the diseases in the set of leaves. Clustering scheme is offered from segmented image of the diseased leaf. Otsu segmentation is measured the size of segmented leaf are uploaded to less storage place. In observing location, the amounts are retrieved as well as the features are extracted from the original segmented image. The enhancement as well as classification is used to SVM based on PSO classifier. The overall design of this paper is LDDS take scan be calculated in terms of system efficiency and it is compared with the existing methods. The result indicates the research technique offers a whole detection accuracy of 90.5% and classification accuracy of 90.4%.
Keywords: Leaf Diseases Detection, Otsu Segmentation, PSO.