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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Vydáno v:	IEEE geoscience and remote sensing letters Ročník 22; s. 1 - 5
Hlavní autoři:	Wu, Di, Liu, Xi, Bai, Song, Liu, Caixia
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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
ISSN:	1545-598X, 1558-0571
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