Automatic Detection and Classification of Brinjal Leaf DiseaSES
S. Mary Cynthia1, L. M. Merlin Livingston2
1S. Mary Cynthia, Assistant Professor, Department of Electronics and Communication Engineering, Jeppiaar Institute of Technology, Chennai, Tamilnadu, India.
2L. M. Merlin Livingston, Professor, Department of Electronics and Communication Engineering, Jeppiaar Institute of Technology, Chennai, Tamilnadu, India.
Manuscript received on 26 August 2019. | Revised Manuscript received on 08 September 2019. | Manuscript published on 30 September 2019. | PP: 3647-3650 | Volume-8 Issue-11, September 2019. | Retrieval Number: K17480981119/2019©BEIESP | DOI: 10.35940/ijitee.K1748.0981119
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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: The diseases in the Brinjal can be identified through the symptoms occur in Brinjal leaf. The indication in touch difference bin of various plant diseases. The designation of disease detection need the specialist’s opinion. The inappropriate identification can result in tremendous quantity of economic loss for farmers. Rather than manual identification, computers are accustomed to give automatic detection and classifying differing kinds of diseases. In this paper, lesion areas affected by diseases are segmented using different techniques, namely DeltaE, Otsu, FCM, k-means algorithm are employed. The proposed method is the image blend by discrete wavelet transforms to increase the excellence of image and reduce uncertainty and redundancy for identification and assessment of agricultural yield which can be done by DeltaE. Further color, texture and structural based features are mixed collectively for getting better performance when compared with single feature extraction.
Keywords: Wavelet Transform, DeltaE, FCM, Segmentation
Scope of the Article: Classification