Citation: Sung, W.-T.; Isa, I.G.T.; Hsiao, S.-J. Designing Aquaculture Monitoring System Based on Data Fusion through Deep Reinforcement Learning (DRL). Electronics 2023, 12, 2032. https://doi.org/10.3390/ electronics12092032 Academic Editors: Dawid Polap, Robertas Damasevicius and Hafiz Tayyab Rauf Received: 6 April 2023 Revised: 21 April 2023 Accepted: 24 April 2023 Published: 27 April 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). electronics Article Designing Aquaculture Monitoring System Based on Data Fusion through Deep Reinforcement Learning (DRL) Wen-Tsai Sung 1 , Indra Griha Tofik Isa 1 and Sung-Jung Hsiao 2, * 1 Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung City 411030, Taiwan; songchen@ncut.edu.tw(W.-T.S.); indraisa89@gm.student.ncut.edu.tw (I.G.T.I.) 2 Departmentof Information Technology, Takming University of Science and Technology, Taipei City 11451, Taiwan * Correspondence: sungjung@gs.takming.edu.tw; Tel.: +886-2-2658-5801 (ext. 5203) Abstract: The aquaculture production sector is one of the suppliers of global food consumption needs. Countries that have a large amount of water contribute to the needs of aquaculture production, especially the freshwater fisheries sector. Indonesia is a country that has a large number of large bodies of water and is the top-five producer of aquaculture production. Technology and engineering continue to be developed to improve the quality and quantity of aquaculture production. One aspect that can be observed is how the condition of fish pond water is healthy and supports fish growth. Various studies have been conducted related to the aquaculture monitoring system, but the problem is how effective it is in terms of accuracy of the resulting output, implementation, and costs. In this research, data fusion (DF) and deep reinforcement learning (DRL) were implemented in an aquaculture monitoring system with temperature, turbidity, and pH parameters to produce valid and accurate output. The stage begins with testing sensor accuracy as part of sensor quality validation, then integrating sensors with wireless sensor networks (WSNs) so they can be accessed in real time. The implemented DF is divided into three layers: first, the signal layer consists of WSNs and their components. Second, the feature layer consists of DRL combined with deep learning (DL). Third, the decision layer determines the output of the condition of the fish pond in “normal” or “not normal” conditions. The analysis and testing of this system look at several factors, i.e., (1) the accuracy of the performance of the sensors used; (2) the performance of the models implemented; (3) the comparison of DF-DRL-based systems with rule-based algorithm systems; and (4) the cost effectiveness compared to labor costs. Of these four factors, the DF-DRL-based aquaculture monitoring system has a higher percentage value and is a low-cost alternative for an accurate aquaculture monitoring system. Keywords: aquaculture monitoring system; data fusion; deep reinforcement learning; deep learning; Internet of Things (IoT) 1. Introduction Indonesia is a fish-producing country which is ranked in the top five in the world. Based on data from Statistics Indonesia (BPS), it is stated that production in the aquaculture sector will reach 14 million tons in 2022 [1]. The potential for increased production from the aquaculture sector will contribute to improving the economy in Indonesia, where in 2022 Indonesia will become an exporter of fish with an achievement of 1.2 million tons [2]. The main issues in aquaculture, especially freshwater aquaculture, are temperature conditions, pH levels, dissolved oxygen levels, and turbidity levels of pond water. This influences optimal fish growth and increases the productivity of fish yields. Aquaculture technology integrated with the Internet of Things (IoT) has been developed, such as a water monitoring system [3], smart aquaculture system [4], AI IoT-based buoy system [5], temperature water control [6], and others. The IoT system being built is a multi-sensor integration that reads data from the environment and then processes it into a control application, such Electronics 2023, 12, 2032. https://doi.org/10.3390/electronics12092032 https://www.mdpi.com/journal/electronics