AN ENSEMBLE LEARNING APPROACH FOR EARLY DETECTION AND CLASSIFICATION OF PLANT DISEASES
Abstract
Plant diseases are a major threat to agriculture, particularly impacting the yield and quality of tomato crops. Early detection and accurate classification of these diseases are essential for effective management and mitigation. Traditional methods of disease detection are often labor-intensive and time-consuming. Although individual convolutional neural networks (CNNs) have shown promise in automated plant disease detection, their accuracy and robustness can be limited when used in isolation. This study proposes an ensemble learning approach that combines three state-of-the-art CNN architectures: AlexNet, ResNet50, and VGG16. A comprehensive dataset of tomato leaf images, categorized into bacterial, viral, fungal diseases, and healthy leaves, was used. Images were preprocessed and augmented to improve model generalization. Each model was trained separately, and their outputs were integrated using a weighted averaging mechanism to form the ensemble model. The weights for each model were optimized based on validation performance. The ensemble model significantly improved classification accuracy compared to individual models. The combined approach achieved an overall accuracy of 97.5%, with precision, recall, and F1-score exceeding 95% for all disease categories. Specifically, the accuracy for detecting bacterial diseases was 96.8%, viral diseases 97.2%, and fungal diseases 97.9%. The ensemble method demonstrated superior robustness and reliability in classifying diverse disease symptoms.

Authors
Neelima Priyanka Nutulapati1, G. Adiline Macriga2, Dileep Pulugu3, R. Thiru Murugan4
SRK Institute of Technology, India1, Sri Sairam Engineering College, India2, Malla Reddy College of Engineering and Technology, India3, Kalasalingam Academy of Research and Education, India4

Keywords
Plant Disease Detection, Ensemble Learning, AlexNet, ResNet50, VGG16
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Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 14 , Issue: 4 , Pages: 3245 - 3250 )
Date of Publication :
May 2024
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79
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