SWRD–YOLO: A Lightweight Instance Segmentation Model for Estimating Rice Lodging Degree in UAV Remote Sensing Images with Real-Time Edge Deployment
Rice lodging severely affects crop growth, yield, and mechanized harvesting efficiency. The accurate detection and quantification of lodging areas are crucial for precision agriculture and timely field management. However, Unmanned Aerial Vehicle (UAV)-based lodging detection faces challenges such a...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/15/15/1570 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849770969385664512 |
|---|---|
| author | Chunyou Guo Feng Tan |
| author_facet | Chunyou Guo Feng Tan |
| author_sort | Chunyou Guo |
| collection | DOAJ |
| description | Rice lodging severely affects crop growth, yield, and mechanized harvesting efficiency. The accurate detection and quantification of lodging areas are crucial for precision agriculture and timely field management. However, Unmanned Aerial Vehicle (UAV)-based lodging detection faces challenges such as complex backgrounds, variable lighting, and irregular lodging patterns. To address these issues, this study proposes SWRD–YOLO, a lightweight instance segmentation model that enhances feature extraction and fusion using advanced convolution and attention mechanisms. The model employs an optimized loss function to improve localization accuracy, achieving precise lodging area segmentation. Additionally, a grid-based lodging ratio estimation method is introduced, dividing images into fixed-size grids to calculate local lodging proportions and aggregate them for robust overall severity assessment. Evaluated on a self-built rice lodging dataset, the model achieves 94.8% precision, 88.2% recall, 93.3% mAP@0.5, and 91.4% F1 score, with real-time inference at 16.15 FPS on an embedded NVIDIA Jetson Orin NX device. Compared to the baseline YOLOv8n-seg, precision, recall, mAP@0.5, and F1 score improved by 8.2%, 16.5%, 12.8%, and 12.8%, respectively. These results confirm the model’s effectiveness and potential for deployment in intelligent crop monitoring and sustainable agriculture. |
| format | Article |
| id | doaj-art-caf4e5fcb7dc498d8a94ec2365a0c677 |
| institution | DOAJ |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-caf4e5fcb7dc498d8a94ec2365a0c6772025-08-20T03:02:47ZengMDPI AGAgriculture2077-04722025-07-011515157010.3390/agriculture15151570SWRD–YOLO: A Lightweight Instance Segmentation Model for Estimating Rice Lodging Degree in UAV Remote Sensing Images with Real-Time Edge DeploymentChunyou Guo0Feng Tan1College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaCollege of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaRice lodging severely affects crop growth, yield, and mechanized harvesting efficiency. The accurate detection and quantification of lodging areas are crucial for precision agriculture and timely field management. However, Unmanned Aerial Vehicle (UAV)-based lodging detection faces challenges such as complex backgrounds, variable lighting, and irregular lodging patterns. To address these issues, this study proposes SWRD–YOLO, a lightweight instance segmentation model that enhances feature extraction and fusion using advanced convolution and attention mechanisms. The model employs an optimized loss function to improve localization accuracy, achieving precise lodging area segmentation. Additionally, a grid-based lodging ratio estimation method is introduced, dividing images into fixed-size grids to calculate local lodging proportions and aggregate them for robust overall severity assessment. Evaluated on a self-built rice lodging dataset, the model achieves 94.8% precision, 88.2% recall, 93.3% mAP@0.5, and 91.4% F1 score, with real-time inference at 16.15 FPS on an embedded NVIDIA Jetson Orin NX device. Compared to the baseline YOLOv8n-seg, precision, recall, mAP@0.5, and F1 score improved by 8.2%, 16.5%, 12.8%, and 12.8%, respectively. These results confirm the model’s effectiveness and potential for deployment in intelligent crop monitoring and sustainable agriculture.https://www.mdpi.com/2077-0472/15/15/1570rice lodging severity estimationUAV-based precision agricultureagricultural image segmentationlightweight deep learningedge AI deploymentefficient inference |
| spellingShingle | Chunyou Guo Feng Tan SWRD–YOLO: A Lightweight Instance Segmentation Model for Estimating Rice Lodging Degree in UAV Remote Sensing Images with Real-Time Edge Deployment Agriculture rice lodging severity estimation UAV-based precision agriculture agricultural image segmentation lightweight deep learning edge AI deployment efficient inference |
| title | SWRD–YOLO: A Lightweight Instance Segmentation Model for Estimating Rice Lodging Degree in UAV Remote Sensing Images with Real-Time Edge Deployment |
| title_full | SWRD–YOLO: A Lightweight Instance Segmentation Model for Estimating Rice Lodging Degree in UAV Remote Sensing Images with Real-Time Edge Deployment |
| title_fullStr | SWRD–YOLO: A Lightweight Instance Segmentation Model for Estimating Rice Lodging Degree in UAV Remote Sensing Images with Real-Time Edge Deployment |
| title_full_unstemmed | SWRD–YOLO: A Lightweight Instance Segmentation Model for Estimating Rice Lodging Degree in UAV Remote Sensing Images with Real-Time Edge Deployment |
| title_short | SWRD–YOLO: A Lightweight Instance Segmentation Model for Estimating Rice Lodging Degree in UAV Remote Sensing Images with Real-Time Edge Deployment |
| title_sort | swrd yolo a lightweight instance segmentation model for estimating rice lodging degree in uav remote sensing images with real time edge deployment |
| topic | rice lodging severity estimation UAV-based precision agriculture agricultural image segmentation lightweight deep learning edge AI deployment efficient inference |
| url | https://www.mdpi.com/2077-0472/15/15/1570 |
| work_keys_str_mv | AT chunyouguo swrdyoloalightweightinstancesegmentationmodelforestimatingricelodgingdegreeinuavremotesensingimageswithrealtimeedgedeployment AT fengtan swrdyoloalightweightinstancesegmentationmodelforestimatingricelodgingdegreeinuavremotesensingimageswithrealtimeedgedeployment |