Disease Detection in Plants using a Pseudo Color Co-Occurrence Matrix
Jibrael Jos1, K A Venkatesh2

1Jibrael Jos*, Department of Computer Science, Christ University, Bengaluru, India.
2K A Venkatesh, Math and Computer Science, Myanmar Institute of Information Technology, Mandalay Myanmar. 

Manuscript received on March 30, 2020. | Revised Manuscript received on April 05, 2020. | Manuscript published on April 30, 2020. | PP: 1485-1490 | Volume-9 Issue-4, April 2020. | Retrieval Number: D7488049420/2020©BEIESP | DOI: 10.35940/ijeat.D7488.049420
Open Access | Ethics and Policies | Cite | Mendeley
© 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: This study reports a color based texture classification for a machine vision system for the identification of disease in plants from color leaf images. We applied the texture features in literature and studied which subset will be effective for Mango and Tomato plants. Effectiveness of each statistical functions were studied in classifying the pattern using a Support Vector Machine. For textures which are different like smooth new leaves, dry leaves and growth Gray Level Co-occurrence based statistics was effective but values failed to discriminate in tomato diseases. We propose a novel method which uses second order statistics on a pseudo color based co-occurrence matrix which resulted in a better classification for three tomato diseases. This method can be applied for early Disease Detection for any plant and help farmers take corrective measures to avoid loss of yield.
Keywords: CCM, Disease Detection, Pattern Classification, Texture Analysis, Tomato Plants.