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.
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
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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43
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