Deep reinforcement learning enables adaptive-image augmentation for automated optical inspection of plant rust

Deep reinforcement learning enables adaptive-image augmentation for automated optical inspection of plant rust

This study proposes an adaptive image augmentation scheme using deep reinforcement learning (DRL) to improve the performance of a deep learning-based automated optical inspection system. The study addresses the challenge of inconsistency in the performance of single image augmentation methods. It in...

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Bibliographic Details
Main Authors: Shiyong Wang, Asad Khan, Ying Lin, Zhuo Jiang, Hao Tang, Suliman Yousef Alomar, Muhammad Sanaullah, Uzair Aslam Bhatti
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-07-01
Series:Frontiers in Plant Science
Subjects:
adaptive image augmentation
deep reinforcement learning
deep Q-learning
automated optical inspection
semantic segmentation
Online Access:https://www.frontiersin.org/articles/10.3389/fpls.2023.1142957/full

Internet

https://www.frontiersin.org/articles/10.3389/fpls.2023.1142957/full

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