Turkish Journal of Computer and Mathematics Education
Journal license

Journal

Turkish Journal of Computer and Mathematics Education


Volume
& Issue

Volume 11, Issue 2


Published
on


Pages

854-858


DOI

Article

Disease Detection and Classification in Cotton Plants using Unsupervised Learning-based Color and Texture Feature Extraction


Authors

Anil Moguram Affiliation:
Siddhartha Institute of Technology and Sciences, Hyderabad, India
, Madiapalli Sumalatha Affiliation:
Associate Professor, Dept. of ECE, Siddhartha Institute of Technology and Sciences, Narapally, Hyderabad, Telangana
and Gattu Sandeep Affiliation:
Associate Professor, Dept. of ECE, Siddhartha Institute of Technology and Sciences, Narapally, Hyderabad, Telangana


Abstract

In this, we have used SVM classifier to identify the pest and type of disease in cotton plant. Image acquisition devices are used to acquire images of plantations at regular intervals. These images are then subjected to pre-processing using median filtering technique. The pre-processed leaf images are then segmented using K-means clustering method. Then the color features(mean, skewness), texture features such as energy, entropy, correlation, contrast, edges are extracted from diseased leaf image using gray scale matrix (GSM) in the texture and then compared with normal cotton leaf image.


Keywords


Citation

Moguram, A., Sumalatha, M., & Sandeep, G. (2020). Disease detection and classification in cotton plants using unsupervised learning-based color and texture feature extraction. Turkish Journal of Computer and Mathematics Education, 11(2), 854–858.

Published by: Engineering Journals

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