# A hybrid deep learning framework using convolutional and transformer models for robust plant disease classification

## TL;DR

This paper introduces a hybrid AI model combining CNNs and transformers to accurately classify plant diseases from leaf images, achieving high performance on a large dataset.

## Contribution

A novel hybrid framework integrating EfficientNet-B7 and ViT-B16 for improved plant disease classification accuracy and robustness.

## Key findings

- The hybrid model achieved 98.13% accuracy on a dataset of 21,534 plant leaf images.
- It outperformed standalone CNNs and other models in precision, recall, and F1-scores across all classes.
- Combining CNNs and transformers effectively captures both local and contextual features for disease detection.

## Abstract

Plant diseases continue to pose a significant threat to worldwide food security, resulting in notable yield reductions and economic consequences. Automated disease diagnosis through machine learning has arisen as a potential solution; nevertheless, current methods frequently have difficulty in capturing both detailed local attributes and overarching contextual patterns found in plant leaf images. This study presents a thorough comparative examination of conventional and deep learning methods—such as Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), YOLO, Support Vector Machines (SVMs), and Random Forests—for the classification of multi-class plant diseases. To overcome the constraints of individual CNN and transformer models, a new hybrid framework that integrates EfficientNet-B7 for strong spatial feature extraction with a Vision Transformer (ViT-B16) for comprehensive contextual modeling is suggested. The system is assessed on an extensive dataset consisting of 21,534 images covering 38 classes of plant diseases and healthy specimens. Experimental findings show that the suggested hybrid model reaches an accuracy of 98.13%, surpassing standalone CNN baselines and other rival models, while consistently achieving high precision, recall, and F1-scores for all classes. The results emphasize the success of combining convolutional and transformer-based models for scalable and precise plant disease detection, aiding the creation of smart decision-support systems for precision farming.