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All Journal International Journal of Informatics and Communication Technology (IJ-ICT)
Penugonda Seetha Rama Krishna
Annamalai University
Computer Science & IT

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Improving the performance of leaf disease detection and classification using beetle swarm optimization technique Penugonda Seetha Rama Krishna; S. Nagarajan
International Journal of Informatics and Communication Technology (IJ-ICT) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijict.v15i3.pp967-974

Abstract

The timely identification and diagnosis of leaf diseases is crucial for crop productivity and health. This study proposes a robust approach to this issue by combining beetle swarm optimization (BSO) with other ML models. Four different datasets were used to train our model: apple leaf, grape leaf, plant village leaf, and tomato leaf for disease detection. The process begins with preparing the leaf images, involving contrast enhancement and noise reduction. Through color-based segmentation, we can distinguish healthy regions from diseased ones, aiding in the classification process. Our research demonstrates the effectiveness of the BSO-convolutional neural networks (CNN) method in recognizing and categorizing plant diseases with high accuracy rates. Leveraging the power of BSO to adjust the model’s parameters and incorporating color-based segmentation enhances the model’s robustness and accuracy. The results of this study highlight the potential of automated disease management systems for agriculture, providing agronomists and farmers with the necessary tools to address and monitor emerging threats to their crops effectively.
Co-Authors S. Nagarajan