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A deep learning based approach for automated plant disease classification using vision transformer.
Yasamin Borhani1, Javad Khoramdel2, Esmaeil Najafi3
1Center of Excellence in Robotics and Control, Advanced Robotics & Automated Systems (ARAS), Faculty of Mechanical Engineering, K. N. Toosi University of Technology, Tehran, Iran.
This study introduces a Vision Transformer (ViT) for automated plant disease classification, aiding farmers with visual diagnostics. Combining ViT with CNNs balances accuracy and prediction speed for practical agricultural applications.
Area of Science:
- Agricultural Science
- Computer Science
- Machine Learning
Background:
- Plant diseases significantly reduce agricultural yields, necessitating effective diagnostic tools.
- Farmers require accessible visual information for timely disease identification and management.
Purpose of the Study:
- To develop a real-time automated plant disease classification system using deep learning.
- To evaluate the performance of Vision Transformer (ViT) and Convolutional Neural Network (CNN) models for this task.
Main Methods:
- Implemented a lightweight Vision Transformer (ViT) model for plant disease classification.
- Compared ViT performance against classical CNN methods and hybrid CNN-ViT approaches.
- Trained and evaluated models on multiple plant disease datasets.
Main Results:
- Vision Transformer models demonstrated increased classification accuracy.
- Attention mechanisms in ViT models led to slower prediction times.
- Hybrid CNN-ViT models effectively compensated for the speed reduction caused by attention blocks.
Conclusions:
- Deep learning, particularly ViT, offers a promising approach for automated plant disease identification.
- Hybrid models combining CNN and ViT architectures provide a viable solution for balancing accuracy and real-time performance in agricultural applications.
- Optimized model architectures are crucial for practical deployment in farming environments.