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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Vydáno v:	Frontiers in plant science Ročník 15; s. 1448669
Hlavní autoři:	Chen, Yayong, Zhou, Beibei, Xiaopeng, Chen, Ma, Changkun, Cui, Lei, Lei, Feng, Han, Xiaojie, Chen, Linjie, Wu, Shanshan, Ye, Dapeng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland Frontiers Media SA 2024 Frontiers Media S.A
Témata:	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
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Popis
Shrnutí:	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 and network structures, which is low practicality in unstructured environments, e.g., dry thermal valley environment (DTV). Therefore, this research combined a transfer learning (MTPI, maximum transfer potential index method) and an RL (the MTSA reinforcement learning, Multi-Thompson Sampling Algorithm) in dataset auto-augmentation and networks auto-training to reduce human experience and T&E. Firstly, to maximize the iteration speed and minimize the dataset consumption, the best iteration conditions (MTPI conditions) were derived with the improved MTPI method, which shows that subsequent iterations required only 2.30% dataset and 6.31% time cost. Then, the MTSA was improved under MTPI conditions (MTSA-MTPI) to auto-augmented datasets, and the results showed a 16.0% improvement in accuracy (human error) and a 20.9% reduction in standard error (T&E cost). Finally, the MTPI-MTSA was used for four networks auto-training (e.g., FCN, Seg-Net, U-Net, and Seg-Res-Net 50) and showed that the best Seg-Res-Net 50 gained 95.2% WPA (accuracy) and 90.9% WIoU. This study provided an effective auto-training method for complex vegetation information collection, which provides a reference for reducing the manual intervention of deep learning.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Edited by: Zenghui Zhang, Shanghai Jiao Tong University, China Weiwei Guo, Tongji University, China Reviewed by: Weitao Chen, China University of Geosciences Wuhan, China These authors have contributed equally to this work
ISSN:	1664-462X 1664-462X
DOI:	10.3389/fpls.2024.1448669