Enhanced disease detection in plant leaves using segmentation and feature extraction techniques

Vijayabharathi Durairaj; Chellammal Surianarayanan

doi:10.26637/MJM0804/0144

Abstract

Plant diseases will decrease the yield, which will have a negative impact on the economy. Image processing techniques are essential for efficient detection of diseases in plants. Despite the existence of various research works for detection of plant diseases, obtaining enhanced accuracy is still an open challenge. In this work, kernel based Fuzzy C-Means clustering for segmentation along with Discrete Wavelet Transform based feature extraction and deep learning based autoencoder based classification are employed towards enhancing efficient detection of diseases in plant leaves. To test the proposed approach, experimentation has been carried out with different datasets in two different manners, namely, (i) with segmentation and (ii) without segmentation. Results are compared and recommendations are suggested.

Keywords:

Kernel based Fuzzy C-Means Clustering, Discrete Wavelet Transform based feature extraction, deep learning based autoencoder, disease detection in plant leaves

Mathematics Subject Classification:

Mathematics

Authors Imprint References Funding Related Articles Downloads

Vijayabharathi Durairaj Department of Computer Science, Bharathidasan University Constituent Arts and Science College [Affiliated to Bharathidasan University], Navalurkuttapattu, Tiruchirappalli-620027, Tamil Nadu, India.
Chellammal Surianarayanan Vijayabharathi Durairaj1

Pages: 2173-2176
Date Published: 01-10-2020
Vol. 8 No. 04 (2020): Malaya Journal of Matematik (MJM)

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