Integrated Aquaculture Monitoring System Using Combined Wireless Sensor Networks and Deep Reinforcement Learning

Abstract

Freshwater fish is one of the commodities experiencing an increasing growth rate from 1990 to 2018. Many efforts have been made to meet market needs, through both fisheries technology and applied technology, one of which is an integrated monitoring system. In this study, an aquaculture monitoring system was developed that integrates wireless sensor networks (WSNs) based on temperature, pH, and turbidity with deep reinforcement learning. The purpose of this study is to produce a convenient, precise, and low-cost aquaculture monitoring system. The stages of the study are (1) the integration of all the WSN components, (2) the validation of the WSNs, (3) the implementation of the analysis model in the system, (4) the implementation of the recommended model into the DRL system, and (5) practical experimentation using the aquaculture monitoring system. The WSN validation results indicate that the average percentage error is 3.23%, whereas at the system modeling stage, the optimal accuracy is 98.80%. In the experiment to monitor real aquaculture environmental conditions, an accuracy of 97% is obtained.