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Effective feature selection based HOBS pruned- ELM model for tomato plant leaf disease classification.
1School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nādu, India.
Plos One
|December 5, 2024
Summary
A new lightweight deep learning model accurately classifies tomato plant diseases in real-time. This optimized convolutional neural network (CNN) significantly reduces processing time and model size, outperforming existing methods for improved crop yield potential.
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Area of Science:
- Agricultural Science
- Computer Science
- Deep Learning
Background:
- Tomato cultivation faces significant yield losses due to biotic and abiotic stresses.
- Traditional deep learning models for plant disease classification are computationally intensive, hindering real-time applications.
- Existing methods often struggle with efficiency and accuracy in complex agricultural environments.
Purpose of the Study:
- To develop a lightweight convolutional neural network (CNN) for real-time biotic stress classification in tomato leaves.
- To address the limitations of conventional CNNs, focusing on reduced processing complexity and enhanced accuracy.
- To create an efficient model for early disease detection in tomato plants, crucial for sustainable agriculture.
Main Methods:
- Proposed a novel lightweight CNN architecture incorporating Elephant Herding Optimization (EHO) for feature selection.
- Integrated Hessian-based Optimal Brain Surgeon (HOBS) with a pruned Extreme Learning Machine (ELM) for parameter optimization.
- Utilized the Plant Village dataset with 8,000 images across 10 tomato plant classes for training and validation.
Main Results:
- Achieved a high accuracy of 95.73% and Cohen's kappa of 0.81% with a training time of 2.35 seconds.
- The pruned model demonstrated a reduced size of 9.2 MB and fewer parameters compared to conventional models.
- Outperformed existing models like pruned DenseNet (86.64% accuracy, 10.6 MB), GhostNet (92.15% accuracy, 10.9 MB), and CACPNET (92.4% accuracy, 18.0 MB).
Conclusions:
- The developed lightweight CNN framework offers superior accuracy and processing efficiency for real-time tomato plant disease classification.
- The optimization techniques significantly reduce computational complexity, making the model suitable for practical, on-field applications.
- This approach holds promise for enhancing disease management strategies and improving tomato crop yield potential through early and accurate detection.