Attention Score-Based Multi-Vision Transformer Technique for Plant Disease Classification.

Abstract

This study proposes an advanced plant disease classification framework leveraging the Attention Score-Based Multi-Vision Transformer (Multi-ViT) model. The framework introduces a novel attention mechanism to dynamically prioritize relevant features from multiple leaf images, overcoming the limitations of single-leaf-based diagnoses. Building on the Vision Transformer (ViT) architecture, the Multi-ViT model aggregates diverse feature representations by combining outputs from multiple ViTs, each capturing unique visual patterns. This approach allows for a holistic analysis of spatially distributed symptoms, crucial for accurately diagnosing diseases in trees. Extensive experiments conducted on apple, grape, and tomato leaf disease datasets demonstrate the model's superior performance, achieving over 99% accuracy and significantly improving F 1 scores compared to traditional methods such as ResNet, VGG, and MobileNet. These findings underscore the effectiveness of the proposed model for precise and reliable plant disease classification.

Plant phenotyping relevance

植物葉の画像から病徴・病害状態を分類する新規Vision Transformer手法を開発し、複数データセットと既存モデルで性能比較しているため、植物フェノタイピング手法が中心です。

Code and data availability

The paper's plant disease classification experiments use a publicly available Kaggle leaf image dataset, explicitly named in the Data Availability Statement. No author code or model checkpoints are disclosed.