Baek Eu-Tteum
Department of AI & Big Data, Honam University, Gwangju 62399, Republic of Korea.
Sensors (Basel). 2025 Jan 6;25(1):270. doi: 10.3390/s25010270.
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 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.
本研究提出了一种先进的植物病害分类框架，该框架利用基于注意力分数的多视觉Transformer（Multi-ViT）模型。该框架引入了一种新颖的注意力机制，以动态地对来自多个叶片图像的相关特征进行优先级排序，克服了基于单叶诊断的局限性。基于视觉Transformer（ViT）架构，Multi-ViT模型通过组合多个ViT的输出聚合不同的特征表示，每个ViT捕获独特的视觉模式。这种方法允许对空间分布的症状进行整体分析，这对于准确诊断树木病害至关重要。在苹果、葡萄和番茄叶片病害数据集上进行的大量实验证明了该模型的卓越性能，与ResNet、VGG和MobileNet等传统方法相比，准确率超过99%，并显著提高了F1分数。这些发现强调了所提出模型在精确可靠的植物病害分类方面的有效性。
