Attention Score-Based Multi-Vision Transformer Technique for Plant Disease Classification
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 limitatio...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/1/270 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841548961770373120 |
|---|---|
| author | Eu-Tteum Baek |
| author_facet | Eu-Tteum Baek |
| author_sort | Eu-Tteum Baek |
| collection | DOAJ |
| description | 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 <i>F</i>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. |
| format | Article |
| id | doaj-art-ff28e50d0a254ef096d4ec3bcea9ce70 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-ff28e50d0a254ef096d4ec3bcea9ce702025-01-10T13:21:25ZengMDPI AGSensors1424-82202025-01-0125127010.3390/s25010270Attention Score-Based Multi-Vision Transformer Technique for Plant Disease ClassificationEu-Tteum Baek0Department of AI & Big Data, Honam University, Gwangju 62399, Republic of KoreaThis 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 <i>F</i>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.https://www.mdpi.com/1424-8220/25/1/270attention mechanismplant pathologydeep learning in agriculturemulti-modal disease detectionvision-based diagnosis |
| spellingShingle | Eu-Tteum Baek Attention Score-Based Multi-Vision Transformer Technique for Plant Disease Classification Sensors attention mechanism plant pathology deep learning in agriculture multi-modal disease detection vision-based diagnosis |
| title | Attention Score-Based Multi-Vision Transformer Technique for Plant Disease Classification |
| title_full | Attention Score-Based Multi-Vision Transformer Technique for Plant Disease Classification |
| title_fullStr | Attention Score-Based Multi-Vision Transformer Technique for Plant Disease Classification |
| title_full_unstemmed | Attention Score-Based Multi-Vision Transformer Technique for Plant Disease Classification |
| title_short | Attention Score-Based Multi-Vision Transformer Technique for Plant Disease Classification |
| title_sort | attention score based multi vision transformer technique for plant disease classification |
| topic | attention mechanism plant pathology deep learning in agriculture multi-modal disease detection vision-based diagnosis |
| url | https://www.mdpi.com/1424-8220/25/1/270 |
| work_keys_str_mv | AT eutteumbaek attentionscorebasedmultivisiontransformertechniqueforplantdiseaseclassification |