Diagnosis of Plant Diseases using Artificial Neural Network
Madhukar S. Chavan
Dr. Madhukar. S. Chavan, Electronics and Telecommunication, BATU University, P.V.P.I.T, Budhgaon, Sangli, India.
Manuscript received on 21 August 2019. | Revised Manuscript received on 01 September 2019. | Manuscript published on 30 September 2019. | PP: 1464-1467 | Volume-8 Issue-11, September 2019. | Retrieval Number: J98020881019/2019©BEIESP | DOI: 10.35940/ijitee.J9802.0981119
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Pomegranate is one of India’s most commonly cultivated fruit crops. manual expert observations are being used to detect leaf diseases that take longer time for further prevention. Fruit diseases are causing devastating disadvantages in worldwide agricultural business economic losses in production .in this journal, the answer is proposed and valid by experiment for the identification and classification of fruit disorders. The objective of proposed work is to analyze the illness utilizing picture preparing and artificial intelligence techniques on pictures of pomegranate plant leaf. In the proposed framework, pomegranate leaf picture with complex foundation is taken as input. Then pomegranate leaf ailment division is finished utilizing K-means clustering. The infected segment from portioned pictures is recognized. Best results have been seen when neural networks with a RBFN is used for a classification.
Keywords: Segmentation , Image processing, K-means clustering and feature extraction.
Scope of the Article: Signal and Image Processing