Iterative optimization annotation pipeline and ALSS-YOLO-Seg for efficient banana plantation segmentation in UAV imagery
Precise segmentation of unmanned aerial vehicle (UAV)-captured images plays a vital role in tasks such as crop yield estimation and plant health assessment in banana plantations. By identifying and classifying planted areas, crop areas can be calculated, which is indispensable for accurate yield pre...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-01-01
|
| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2024.1508549/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841553305718751232 |
|---|---|
| author | Ang He Ang He Ximei Wu Ximei Wu Xing Xu Xing Xu Jing Chen Jing Chen Xiaobin Guo Xiaobin Guo Sheng Xu Sheng Xu |
| author_facet | Ang He Ang He Ximei Wu Ximei Wu Xing Xu Xing Xu Jing Chen Jing Chen Xiaobin Guo Xiaobin Guo Sheng Xu Sheng Xu |
| author_sort | Ang He |
| collection | DOAJ |
| description | Precise segmentation of unmanned aerial vehicle (UAV)-captured images plays a vital role in tasks such as crop yield estimation and plant health assessment in banana plantations. By identifying and classifying planted areas, crop areas can be calculated, which is indispensable for accurate yield predictions. However, segmenting banana plantation scenes requires a substantial amount of annotated data, and manual labeling of these images is both timeconsuming and labor-intensive, limiting the development of large-scale datasets. Furthermore, challenges such as changing target sizes, complex ground backgrounds, limited computational resources, and correct identification of crop categories make segmentation even more difficult. To address these issues, we propose a comprehensive solution. First, we designed an iterative optimization annotation pipeline leveraging SAM2’s zero-shot capabilities to generate high-quality segmentation annotations, thereby reducing the cost and time associated with data annotation significantly. Second, we developed ALSS-YOLO-Seg, an efficient lightweight segmentation model optimized for UAV imagery. The model’s backbone includes an Adaptive Lightweight Channel Splitting and Shuffling (ALSS) module to improve information exchange between channels and optimize feature extraction, aiding accurate crop identification. Additionally, a Multi-Scale Channel Attention (MSCA) module combines multi-scale feature extraction with channel attention to tackle challenges of varying target sizes and complex ground backgrounds. We evaluated the zero-shot segmentation performance of SAM2 on the ADE20K and Javeri datasets. Our iterative optimization annotation pipeline demonstrated a significant reduction in manual annotation effort while achieving high-quality segmentation labeling. Extensive experiments on our custom Banana Plantation segmentation dataset show that ALSS-YOLO-Seg achieves state-of-the-art performance. Our code is openly available at https://github.com/helloworlder8/computer_vision. |
| format | Article |
| id | doaj-art-beab93cf7ac1423d8f6dbc6a05bd7411 |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-beab93cf7ac1423d8f6dbc6a05bd74112025-01-09T10:53:10ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-01-011510.3389/fpls.2024.15085491508549Iterative optimization annotation pipeline and ALSS-YOLO-Seg for efficient banana plantation segmentation in UAV imageryAng He0Ang He1Ximei Wu2Ximei Wu3Xing Xu4Xing Xu5Jing Chen6Jing Chen7Xiaobin Guo8Xiaobin Guo9Sheng Xu10Sheng Xu11Guangdong University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, ChinaGuangdong University of Technology, Guangdong Provincial Key Laboratory of Sensing Physics and System Integration Applications, Guangzhou, ChinaGuangdong University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, ChinaGuangdong University of Technology, Guangdong Provincial Key Laboratory of Sensing Physics and System Integration Applications, Guangzhou, ChinaCollege of Engineering, South China Agricultural University, Guangzhou, ChinaSouth China Agricultural University, National Banana Industry Technology System Orchard Production Mechanization Research Laboratory, Guangzhou, ChinaGuangdong University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, ChinaGuangdong University of Technology, Guangdong Provincial Key Laboratory of Sensing Physics and System Integration Applications, Guangzhou, ChinaGuangdong University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, ChinaGuangdong University of Technology, Guangdong Provincial Key Laboratory of Sensing Physics and System Integration Applications, Guangzhou, ChinaGuangdong University of Technology, Guangzhou Higher Education Mega Centre, Guangzhou, ChinaGuangdong University of Technology, Guangdong Provincial Key Laboratory of Sensing Physics and System Integration Applications, Guangzhou, ChinaPrecise segmentation of unmanned aerial vehicle (UAV)-captured images plays a vital role in tasks such as crop yield estimation and plant health assessment in banana plantations. By identifying and classifying planted areas, crop areas can be calculated, which is indispensable for accurate yield predictions. However, segmenting banana plantation scenes requires a substantial amount of annotated data, and manual labeling of these images is both timeconsuming and labor-intensive, limiting the development of large-scale datasets. Furthermore, challenges such as changing target sizes, complex ground backgrounds, limited computational resources, and correct identification of crop categories make segmentation even more difficult. To address these issues, we propose a comprehensive solution. First, we designed an iterative optimization annotation pipeline leveraging SAM2’s zero-shot capabilities to generate high-quality segmentation annotations, thereby reducing the cost and time associated with data annotation significantly. Second, we developed ALSS-YOLO-Seg, an efficient lightweight segmentation model optimized for UAV imagery. The model’s backbone includes an Adaptive Lightweight Channel Splitting and Shuffling (ALSS) module to improve information exchange between channels and optimize feature extraction, aiding accurate crop identification. Additionally, a Multi-Scale Channel Attention (MSCA) module combines multi-scale feature extraction with channel attention to tackle challenges of varying target sizes and complex ground backgrounds. We evaluated the zero-shot segmentation performance of SAM2 on the ADE20K and Javeri datasets. Our iterative optimization annotation pipeline demonstrated a significant reduction in manual annotation effort while achieving high-quality segmentation labeling. Extensive experiments on our custom Banana Plantation segmentation dataset show that ALSS-YOLO-Seg achieves state-of-the-art performance. Our code is openly available at https://github.com/helloworlder8/computer_vision.https://www.frontiersin.org/articles/10.3389/fpls.2024.1508549/fullUAVbanana plantationschanging target sizescomplex ground backgroundsSAM2ALSS |
| spellingShingle | Ang He Ang He Ximei Wu Ximei Wu Xing Xu Xing Xu Jing Chen Jing Chen Xiaobin Guo Xiaobin Guo Sheng Xu Sheng Xu Iterative optimization annotation pipeline and ALSS-YOLO-Seg for efficient banana plantation segmentation in UAV imagery Frontiers in Plant Science UAV banana plantations changing target sizes complex ground backgrounds SAM2 ALSS |
| title | Iterative optimization annotation pipeline and ALSS-YOLO-Seg for efficient banana plantation segmentation in UAV imagery |
| title_full | Iterative optimization annotation pipeline and ALSS-YOLO-Seg for efficient banana plantation segmentation in UAV imagery |
| title_fullStr | Iterative optimization annotation pipeline and ALSS-YOLO-Seg for efficient banana plantation segmentation in UAV imagery |
| title_full_unstemmed | Iterative optimization annotation pipeline and ALSS-YOLO-Seg for efficient banana plantation segmentation in UAV imagery |
| title_short | Iterative optimization annotation pipeline and ALSS-YOLO-Seg for efficient banana plantation segmentation in UAV imagery |
| title_sort | iterative optimization annotation pipeline and alss yolo seg for efficient banana plantation segmentation in uav imagery |
| topic | UAV banana plantations changing target sizes complex ground backgrounds SAM2 ALSS |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1508549/full |
| work_keys_str_mv | AT anghe iterativeoptimizationannotationpipelineandalssyolosegforefficientbananaplantationsegmentationinuavimagery AT anghe iterativeoptimizationannotationpipelineandalssyolosegforefficientbananaplantationsegmentationinuavimagery AT ximeiwu iterativeoptimizationannotationpipelineandalssyolosegforefficientbananaplantationsegmentationinuavimagery AT ximeiwu iterativeoptimizationannotationpipelineandalssyolosegforefficientbananaplantationsegmentationinuavimagery AT xingxu iterativeoptimizationannotationpipelineandalssyolosegforefficientbananaplantationsegmentationinuavimagery AT xingxu iterativeoptimizationannotationpipelineandalssyolosegforefficientbananaplantationsegmentationinuavimagery AT jingchen iterativeoptimizationannotationpipelineandalssyolosegforefficientbananaplantationsegmentationinuavimagery AT jingchen iterativeoptimizationannotationpipelineandalssyolosegforefficientbananaplantationsegmentationinuavimagery AT xiaobinguo iterativeoptimizationannotationpipelineandalssyolosegforefficientbananaplantationsegmentationinuavimagery AT xiaobinguo iterativeoptimizationannotationpipelineandalssyolosegforefficientbananaplantationsegmentationinuavimagery AT shengxu iterativeoptimizationannotationpipelineandalssyolosegforefficientbananaplantationsegmentationinuavimagery AT shengxu iterativeoptimizationannotationpipelineandalssyolosegforefficientbananaplantationsegmentationinuavimagery |