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 - Chen, Yayong; Zhou, Beibei; Xiaopeng, Chen; Ma, Changkun; Cui, Lei; Lei, Feng; Han, Xiaojie; Chen, Linjie; Wu, Shanshan; Ye, Dapeng (Frontiers in Plant Science, 2025)
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, manually parameter training of deep networks may require human experience and T&E (trial-and-error) costs. Moreover, existing auto-trainings adapt to simple datasets and network structures, which is low practicality in complex unstructured environments, e.g., vegetation in 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 reduced 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 was found 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 method was used for four commonly 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 the deep network.