A method of deep network auto-training based on the MTPI auto-transfer learning and a reinforcement learning algorithm for vegetation detection in a dry thermal valley environment

UAV image acquisition and deep learning techniques have been widely used in field hydrological monitoring to meet the increasing data volume demand and refined quality. However, manual parameter training requires trial-and-error costs (T&E), and existing auto-trainings adapt to simple datasets a...

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Veröffentlicht in:	Frontiers in plant science Jg. 15; S. 1448669
Hauptverfasser:	Chen, Yayong, Zhou, Beibei, Xiaopeng, Chen, Ma, Changkun, Cui, Lei, Lei, Feng, Han, Xiaojie, Chen, Linjie, Wu, Shanshan, Ye, Dapeng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland Frontiers Media SA 2024 Frontiers Media S.A
Schlagworte:	Accuracy Algorithms Altitude auto-DL method Automation Cameras Data augmentation data augmentation automatic Data collection Datasets Deep learning Environmental monitoring Error reduction Genetic algorithms Image acquisition Machine learning Methods Network management systems network training automatic Plant Science reinforcement learning for DL segmentation deep learning Standard error Transfer learning Unmanned aerial vehicles Validity Valleys Vegetation vegetation detection China Shenzhen China data augmentation automatic vegetation detection reinforcement learning for DL segmentation deep learning auto-DL method network training automatic
ISSN:	1664-462X, 1664-462X
Online-Zugang:	Volltext
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