Grape Disease Detection Using Transformer-Based Integration of Vision and Environmental Sensing
This study proposes a novel Transformer-based multimodal fusion framework for grape disease detection, integrating RGB images, hyperspectral data, and environmental sensor readings. Unlike traditional single-modal approaches, the proposed method leverages a Transformer-based architecture to effectiv...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
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| Series: | Agronomy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4395/15/4/831 |
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| Summary: | This study proposes a novel Transformer-based multimodal fusion framework for grape disease detection, integrating RGB images, hyperspectral data, and environmental sensor readings. Unlike traditional single-modal approaches, the proposed method leverages a Transformer-based architecture to effectively capture spatial, spectral, and environmental dependencies, improving disease detection accuracy under varying conditions. A comprehensive dataset was collected, incorporating diverse lighting, humidity, and temperature conditions, and enabling robust performance evaluation. Experimental results demonstrate that the proposed method outperforms state-of-the-art (SOTA) models, achieving an mAP@50 of 0.94, an mAP@75 of 0.93, Precision of 0.93, and Recall of 0.95, surpassing leading detection baselines. The results confirm that the integration of multimodal information significantly enhances disease detection robustness and generalization, offering a promising solution for real-world vineyard disease management. |
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| ISSN: | 2073-4395 |