Explainable vision transformer enabled convolutional neural network for plant disease identification: PlantXViT

TL;DR

This paper introduces PlantXViT, a lightweight, explainable vision transformer-based CNN model that effectively detects plant diseases across multiple crops, outperforming existing methods with high accuracy and suitability for IoT applications.

Contribution

The study presents a novel hybrid model combining CNNs and vision transformers for plant disease detection, with a lightweight design and enhanced explainability, addressing gaps in transformer applications in plant pathology.

Findings

Achieves over 93.55% accuracy on Apple dataset

Outperforms five state-of-the-art methods on five datasets

Model is lightweight with only 0.8 million parameters

Abstract

Plant diseases are the primary cause of crop losses globally, with an impact on the world economy. To deal with these issues, smart agriculture solutions are evolving that combine the Internet of Things and machine learning for early disease detection and control. Many such systems use vision-based machine learning methods for real-time disease detection and diagnosis. With the advancement in deep learning techniques, new methods have emerged that employ convolutional neural networks for plant disease detection and identification. Another trend in vision-based deep learning is the use of vision transformers, which have proved to be powerful models for classification and other problems. However, vision transformers have rarely been investigated for plant pathology applications. In this study, a Vision Transformer enabled Convolutional Neural Network model called "PlantXViT" is proposed for plant disease identification. The proposed model combines the capabilities of traditional convolutional neural networks with the Vision Transformers to efficiently identify a large number of plant diseases for several crops. The proposed model has a lightweight structure with only 0.8 million trainable parameters, which makes it suitable for IoT-based smart agriculture services. The performance of PlantXViT is evaluated on five publicly available datasets. The proposed PlantXViT network performs better than five state-of-the-art methods on all five datasets. The average accuracy for recognising plant diseases is shown to exceed 93.55%, 92.59%, and 98.33% on Apple, Maize, and Rice datasets, respectively, even under challenging background conditions. The efficiency in terms of explainability of the proposed model is evaluated using gradient-weighted class activation maps and Local Interpretable Model Agnostic Explanation.