Deep reinforcement learning in agricultural IoT-based: A review

¹ Ph.D. Program Prospective Technology of Electrical Engineering and Computer Science, National Chin-Yi University of Technology, Taichung, 411030, Taiwan
² Graduate Institute of Precision Manufacturing, National Chin-Yi University of Technology, Taichung, 411030, Taiwan

^* Corresponding author: indraisa89@gm.student.ncut.edu.tw

Abstract

The world’s food needs have an impact on innovation in the field of agriculture, and one of them is by implementing deep reinforcement learning (DRL) technology, which is very relevant to the Industrial Revolution 4.0. This research discusses important issues and developments in DRLs that are implemented, especially in the field of IoT-based agriculture. The research method uses a Systematic Literature Review (SLR) approach through searching and analysing raw data sources, sorting and selecting relevant data relevant to the topics discussed, discussing topic areas and how trends are in current conditions, and concluding. The purpose of this study is to see how the current state of DRL implementation in agricultural IoT-based. The limitations of the study are that (1) the data sources come from Scopus-indexed journals; (2) the journal period is 2021–2023; (3) the research approach uses SLR; and (4) the focus of the discussion includes the implementation of DRL in agricultural IoT-based systems, the development of DRL technology, and the use of tools in DRL.