Fine Tuning Large Models for Straw Detection in Harvested Fields Under Few-Shot Learning Scenarios
The effective monitoring of crop residues, particularly straw in non-harvested fields, is essential for sustainable agricultural practices and environmental management. Traditional methods of straw detection often face challenges due to limited training data, high annotation complexity, and the need...
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| Vydané v: | IEEE geoscience and remote sensing letters Ročník 22; s. 1 - 5 |
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| Abstract | The effective monitoring of crop residues, particularly straw in non-harvested fields, is essential for sustainable agricultural practices and environmental management. Traditional methods of straw detection often face challenges due to limited training data, high annotation complexity, and the need for accurate feature recognition. In response to these challenges, this study investigates the effectiveness of the segment anything model (SAM)-vision transformer (ViT)-huge-low-rank adaptation (LoRA) method, which leverages few-shot learning techniques to accurately and efficiently identify straw using only 0.65% of the available training data. A comparative analysis was performed under consistent testing conditions against several established algorithms, including Deeplabv3, FCN, PSPNet, TransformerUNet, UNet3+, AFFormer, and DynaMas. The results indicate that the SAM-ViT-huge-LoRA method achieves an <inline-formula> <tex-math notation="LaTeX">F1 </tex-math></inline-formula>-score of 83.6%, exceeding the performance of the second best algorithm by at least 2%. Furthermore, the method demonstrates an intersection over union (IoU) metric of 98.02%, surpassing competing models by a minimum of 25%. This research highlights the potential of few-shot learning in scenarios characterized by data scarcity and complex annotation processes. By effectively fine-tuning large models with a small amount of high-quality training data, our approach addresses the challenges of insufficient sample sizes, optimizes the use of limited datasets, reduces annotation costs, and significantly enhances recognition accuracy. |
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| AbstractList | The effective monitoring of crop residues, particularly straw in non-harvested fields, is essential for sustainable agricultural practices and environmental management. Traditional methods of straw detection often face challenges due to limited training data, high annotation complexity, and the need for accurate feature recognition. In response to these challenges, this study investigates the effectiveness of the segment anything model (SAM)-vision transformer (ViT)-huge-low-rank adaptation (LoRA) method, which leverages few-shot learning techniques to accurately and efficiently identify straw using only 0.65% of the available training data. A comparative analysis was performed under consistent testing conditions against several established algorithms, including Deeplabv3, FCN, PSPNet, TransformerUNet, UNet3+, AFFormer, and DynaMas. The results indicate that the SAM-ViT-huge-LoRA method achieves an [Formula Omitted]-score of 83.6%, exceeding the performance of the second best algorithm by at least 2%. Furthermore, the method demonstrates an intersection over union (IoU) metric of 98.02%, surpassing competing models by a minimum of 25%. This research highlights the potential of few-shot learning in scenarios characterized by data scarcity and complex annotation processes. By effectively fine-tuning large models with a small amount of high-quality training data, our approach addresses the challenges of insufficient sample sizes, optimizes the use of limited datasets, reduces annotation costs, and significantly enhances recognition accuracy. The effective monitoring of crop residues, particularly straw in non-harvested fields, is essential for sustainable agricultural practices and environmental management. Traditional methods of straw detection often face challenges due to limited training data, high annotation complexity, and the need for accurate feature recognition. In response to these challenges, this study investigates the effectiveness of the segment anything model (SAM)-vision transformer (ViT)-huge-low-rank adaptation (LoRA) method, which leverages few-shot learning techniques to accurately and efficiently identify straw using only 0.65% of the available training data. A comparative analysis was performed under consistent testing conditions against several established algorithms, including Deeplabv3, FCN, PSPNet, TransformerUNet, UNet3+, AFFormer, and DynaMas. The results indicate that the SAM-ViT-huge-LoRA method achieves an <inline-formula> <tex-math notation="LaTeX">F1 </tex-math></inline-formula>-score of 83.6%, exceeding the performance of the second best algorithm by at least 2%. Furthermore, the method demonstrates an intersection over union (IoU) metric of 98.02%, surpassing competing models by a minimum of 25%. This research highlights the potential of few-shot learning in scenarios characterized by data scarcity and complex annotation processes. By effectively fine-tuning large models with a small amount of high-quality training data, our approach addresses the challenges of insufficient sample sizes, optimizes the use of limited datasets, reduces annotation costs, and significantly enhances recognition accuracy. |
| Author | Liu, Caixia Wu, Di Liu, Xi Bai, Song |
| Author_xml | – sequence: 1 givenname: Di surname: Wu fullname: Wu, Di organization: School of Surveying and Mapping Engineering, Heilongjiang Institute of Technology, Harbin, China – sequence: 2 givenname: Xi surname: Liu fullname: Liu, Xi organization: Heilongjiang Geomatics Centre of Ministry of Natural Resources, Harbin, China – sequence: 3 givenname: Song surname: Bai fullname: Bai, Song organization: Heilongjiang Geomatics Centre of Ministry of Natural Resources, Harbin, China – sequence: 4 givenname: Caixia orcidid: 0000-0001-5851-6374 surname: Liu fullname: Liu, Caixia email: liucx@radi.ac.cn organization: State Key Laboratory of Remote Sensing Science and Digital Earth, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China |
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| Cites_doi | 10.1109/CVPR.2017.549 10.1007/978-3-031-34048-2_58 10.1609/aaai.v37i1.25126 10.1080/26395940.2021.1948354 10.1145/3582688 10.1109/CVPR.2017.660 10.1038/s41598-024-60375-1 10.1109/CVPR.2015.7298965 10.1016/j.media.2024.103280 10.1109/CVPR52729.2023.01085 10.1007/978-3-030-00889-5_1 10.1016/j.rse.2011.09.016 10.3390/rs13071358 10.1007/978-3-319-50835-1_22 10.3390/rs16020342 10.7717/peerj-cs.432 10.3390/su11061762 10.3390/s19183859 |
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| SubjectTerms | Adaptation models Agricultural practices Algorithms Annotations Bibliographic information Comparative analysis Complexity Computational modeling Crop residues Crops Effectiveness Environmental management Feature recognition Few-shot learning Harvesting Image segmentation Learning low-rank adaptation (LoRA) Machine learning Monitoring Remote sensing segment anything model (SAM) semantic segmentation Straw Sustainable agriculture Sustainable practices Training Training data Transformers Vectors |
| Title | Fine Tuning Large Models for Straw Detection in Harvested Fields Under Few-Shot Learning Scenarios |
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