Image based Identification of Leaf Crumple and Leaf Spot Diseases in Cotton Plant
Anush Reddy Kommareddy1, Satya Anirudh Polisetty2, Chetan Sai Kurra3, S. Padmavathi4, Mithun Chandra Pokuri5
1Anush Reddy Kommareddy, Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham Coimbatore (T.N), India.
2Satya Anirudh Polisetty, Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham Coimbatore (T.N), India.
3Chetan Sai Kurra, Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham Coimbatore (T.N), India.
4S. Padmavathi, Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham Coimbatore (T.N), India.
5Mithun Chandra Pokuri, Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham Coimbatore (T.N), India.
Manuscript received on 03 June 2019 | Revised Manuscript received on 28 June 2019 | Manuscript Published on 04 July 2019 | PP: 345-348 | Volume-8 Issue-1S4 June 2019 | Retrieval Number: A10610681S419/2019©BEIESP
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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: Identification of plant diseases based on images derived from computer vision is a major requirement for smart agriculture. Conventional algorithms warrant large dataset for better accuracy. They perform well with large variation in color or explicit probes on a specific disease. This paper considers 4 major diseases of cotton plants with a combination of images with and without color variation. This paper adapted image processing algorithms to extract precise features for classification, highly preferred and apt, when the dataset sizes are limited. Verification results of the proposed method validate its rationale and viability.
Keywords: Cotton Plant Disease Identification, Leaf Disease Feature Extraction, Color Image Enhancement, Histogram Equalization, Color Edge Detection.
Scope of the Article: Image analysis and Processing