Texture based Leaf Disease classification using Machine Learning Techniques 
Shravankumat Arjunagi1, Nagaraj B. Patil2
1Mr. Shravankumar Arjunagi*, Assistant professor in Department of Computer Science and Engineering at APPA Institute of Engineering and Technology Kalaburgi, Karnataka.
2Dr. Nagaraj B. Patil, Associate Professor and HOD Dept. of CSE & ISE at Government College of Engineering, Raichur Karnataka.
Manuscript received on September 22, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 956-961 | Volume-9 Issue-1, October 2019 | Retrieval Number: A9446109119/2019©BEIESP | DOI: 10.35940/ijeat.A9446.109119
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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: Machine learning techniques has emerged as a potential field in many of present day agricultural applications. One of these applications is the identification and classification of leaf diseases. In this paper, a triangular based and OTSU based methods are applied for segmentation, Textural features primarily based on GLCM are obtained for these segmented images using kmeans clustering technique, further classification of different leaf disease is performed using an SVM based classification. The proposed method resulted in an overall classification accuracy of 70% using the triangular based segmentation with an AUC of 0.63.
Keywords: Machinelearning, Triangular, Segmentation, SVM.