Texture Features-Based Detection of Plant Leaf Diseases Using RM-SVM

International Journal of Electronics and Communication Engineering

© 2024 by SSRG - IJECE Journal

Volume 11 Issue 1

Year of Publication : 2024

Authors : G. Nandha Kumar, V. Vijayakumar

10.14445/23488549/IJECE-V11I1P110

How to Cite?

G. Nandha Kumar, V. Vijayakumar, "Texture Features-Based Detection of Plant Leaf Diseases Using RM-SVM," SSRG International Journal of Electronics and Communication Engineering, vol. 11,  no. 1, pp. 131-142, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I1P110

Abstract:

Texture features describe the spatial arrangement of pixels in an image. They can be used to categorize different forms of objects in an image, such as leaves, flowers, and fruits. In the context of plant leaf disease detection, texture features can be used to find the presence of diseases in leaves. In this paper, the proposed Machine Learning (ML) model can be used for texture feature-based detection of plant leaf diseases in a Redundant Multiclass Support Vector Machine (RM-SVM). RMSVM is a Support Vector Machine (SVM) specifically designed for dealing with redundant features. RM-SVM is effective for texture feature-based detection of plant leaf diseases. However, RM-SVM can be computationally expensive to train and can be sensitive to the choice of hyperparameters. To reduce computational costs, K-Means clustering for redundancy removal is used. Finally, the Max-Rule is applied to the fuse score of predicted results to provide better accuracy at 95.6%.

Keywords:

RM-SVM, K-Means clustering, Redundancy removal, Max-rule, Fusing score, Texture feature, Plant leaf disease detection.

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