MSEA-Net: Multi-Scale and Edge-Aware Network for Weed Segmentation
Accurate weed segmentation in Unmanned Aerial Vehicle (UAV) imagery remains a significant challenge in precision agriculture due to environmental variability, weak contextual representation, and inaccurate boundary detection. To address these limitations, we propose the Multi-Scale and Edge-Aware Ne...
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MDPI AG
2025-04-01
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| Series: | AgriEngineering |
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| Online Access: | https://www.mdpi.com/2624-7402/7/4/103 |
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| author | Akram Syed Baifan Chen Adeel Ahmed Abbasi Sharjeel Abid Butt Xiaoqing Fang |
| author_facet | Akram Syed Baifan Chen Adeel Ahmed Abbasi Sharjeel Abid Butt Xiaoqing Fang |
| author_sort | Akram Syed |
| collection | DOAJ |
| description | Accurate weed segmentation in Unmanned Aerial Vehicle (UAV) imagery remains a significant challenge in precision agriculture due to environmental variability, weak contextual representation, and inaccurate boundary detection. To address these limitations, we propose the Multi-Scale and Edge-Aware Network (MSEA-Net), a lightweight and efficient deep learning framework designed to enhance segmentation accuracy while maintaining computational efficiency. Specifically, we introduce the Multi-Scale Spatial-Channel Attention (MSCA) module to recalibrate spatial and channel dependencies, improving local–global feature fusion while reducing redundant computations. Additionally, the Edge-Enhanced Bottleneck Attention (EEBA) module integrates Sobel-based edge detection to refine boundary delineation, ensuring sharper object separation in dense vegetation environments. Extensive evaluations on publicly available datasets demonstrate the effectiveness of MSEA-Net, achieving a mean Intersection over Union (IoU) of 87.42% on the Motion-Blurred UAV Images of Sorghum Fields dataset and 71.35% on the CoFly-WeedDB dataset, outperforming benchmark models. MSEA-Net also maintains a compact architecture with only 6.74 M parameters and a model size of 25.74 MB, making it suitable for UAV-based real-time weed segmentation. These results highlight the potential of MSEA-Net for improving automated weed detection in precision agriculture while ensuring computational efficiency for edge deployment. |
| format | Article |
| id | doaj-art-4e31238d40c5406785702863711cb641 |
| institution | OA Journals |
| issn | 2624-7402 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AgriEngineering |
| spelling | doaj-art-4e31238d40c5406785702863711cb6412025-08-20T02:24:41ZengMDPI AGAgriEngineering2624-74022025-04-017410310.3390/agriengineering7040103MSEA-Net: Multi-Scale and Edge-Aware Network for Weed SegmentationAkram Syed0Baifan Chen1Adeel Ahmed Abbasi2Sharjeel Abid Butt3Xiaoqing Fang4School of Automation, Central South University, Changsha 410083, ChinaSchool of Automation, Central South University, Changsha 410083, ChinaSchool of Computer Science, Central South University, Changsha 410083, ChinaSchool of Automation, Central South University, Changsha 410083, ChinaSchool of Automation, Central South University, Changsha 410083, ChinaAccurate weed segmentation in Unmanned Aerial Vehicle (UAV) imagery remains a significant challenge in precision agriculture due to environmental variability, weak contextual representation, and inaccurate boundary detection. To address these limitations, we propose the Multi-Scale and Edge-Aware Network (MSEA-Net), a lightweight and efficient deep learning framework designed to enhance segmentation accuracy while maintaining computational efficiency. Specifically, we introduce the Multi-Scale Spatial-Channel Attention (MSCA) module to recalibrate spatial and channel dependencies, improving local–global feature fusion while reducing redundant computations. Additionally, the Edge-Enhanced Bottleneck Attention (EEBA) module integrates Sobel-based edge detection to refine boundary delineation, ensuring sharper object separation in dense vegetation environments. Extensive evaluations on publicly available datasets demonstrate the effectiveness of MSEA-Net, achieving a mean Intersection over Union (IoU) of 87.42% on the Motion-Blurred UAV Images of Sorghum Fields dataset and 71.35% on the CoFly-WeedDB dataset, outperforming benchmark models. MSEA-Net also maintains a compact architecture with only 6.74 M parameters and a model size of 25.74 MB, making it suitable for UAV-based real-time weed segmentation. These results highlight the potential of MSEA-Net for improving automated weed detection in precision agriculture while ensuring computational efficiency for edge deployment.https://www.mdpi.com/2624-7402/7/4/103deep learningprecision agricultureweed segmentationunmanned aerial vehicle (UAV)weed management |
| spellingShingle | Akram Syed Baifan Chen Adeel Ahmed Abbasi Sharjeel Abid Butt Xiaoqing Fang MSEA-Net: Multi-Scale and Edge-Aware Network for Weed Segmentation AgriEngineering deep learning precision agriculture weed segmentation unmanned aerial vehicle (UAV) weed management |
| title | MSEA-Net: Multi-Scale and Edge-Aware Network for Weed Segmentation |
| title_full | MSEA-Net: Multi-Scale and Edge-Aware Network for Weed Segmentation |
| title_fullStr | MSEA-Net: Multi-Scale and Edge-Aware Network for Weed Segmentation |
| title_full_unstemmed | MSEA-Net: Multi-Scale and Edge-Aware Network for Weed Segmentation |
| title_short | MSEA-Net: Multi-Scale and Edge-Aware Network for Weed Segmentation |
| title_sort | msea net multi scale and edge aware network for weed segmentation |
| topic | deep learning precision agriculture weed segmentation unmanned aerial vehicle (UAV) weed management |
| url | https://www.mdpi.com/2624-7402/7/4/103 |
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