Lightweight Multispectral Crop-Weed Segmentation for Precision Agriculture
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915299827122176 |
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| author | Galymzhankyzy, Zeynep Martinson, Eric |
| author_facet | Galymzhankyzy, Zeynep Martinson, Eric |
| contents | Efficient crop-weed segmentation is critical for site-specific weed control in precision agriculture. Conventional CNN-based methods struggle to generalize and rely on RGB imagery, limiting performance under complex field conditions. To address these challenges, we propose a lightweight transformer-CNN hybrid. It processes RGB, Near-Infrared (NIR), and Red-Edge (RE) bands using specialized encoders and dynamic modality integration. Evaluated on the WeedsGalore dataset, the model achieves a segmentation accuracy (mean IoU) of 78.88%, outperforming RGB-only models by 15.8 percentage points. With only 8.7 million parameters, the model offers high accuracy, computational efficiency, and potential for real-time deployment on Unmanned Aerial Vehicles (UAVs) and edge devices, advancing precision weed management. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_07444 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Lightweight Multispectral Crop-Weed Segmentation for Precision Agriculture Galymzhankyzy, Zeynep Martinson, Eric Computer Vision and Pattern Recognition Robotics Image and Video Processing I.4.6 Efficient crop-weed segmentation is critical for site-specific weed control in precision agriculture. Conventional CNN-based methods struggle to generalize and rely on RGB imagery, limiting performance under complex field conditions. To address these challenges, we propose a lightweight transformer-CNN hybrid. It processes RGB, Near-Infrared (NIR), and Red-Edge (RE) bands using specialized encoders and dynamic modality integration. Evaluated on the WeedsGalore dataset, the model achieves a segmentation accuracy (mean IoU) of 78.88%, outperforming RGB-only models by 15.8 percentage points. With only 8.7 million parameters, the model offers high accuracy, computational efficiency, and potential for real-time deployment on Unmanned Aerial Vehicles (UAVs) and edge devices, advancing precision weed management. |
| title | Lightweight Multispectral Crop-Weed Segmentation for Precision Agriculture |
| topic | Computer Vision and Pattern Recognition Robotics Image and Video Processing I.4.6 |
| url | https://arxiv.org/abs/2505.07444 |