Automated Redox Monitoring System (ARMS): An Instrument for Measuring Dissolved Oxygen Levels Using a Potential Redox Sensor (ORP) in a Prototype of Shrimp Farming Pond with an Internet-Based Monitoring System

Abstract

A data acquisition system for measuring and storing dissolved oxygen levels has been implemented to monitor water quality levels in shrimp culture media using an internet-based potential redox sensor (ORP). The purpose of this study is to apply ARMS, an instrument for monitoring internet-based potential redox data (dissolved oxygen levels) in shrimp culture media in real-time to determine the condition of water quality. This system is designed using an ORP sensor that uses WIFI communication. In principle, when the ORP sensor is placed in the water in the shrimp culture media, the potential redox data will be detected by the sensor connected to the ESP8266 which processes and transmits data which is then displayed and analyzed on the smartphone interface. In this study, 9 units of shrimp culture media prototype ponds were used for observational tests. The object of observation in the pond is water quality data in the form of sediment potential redox values. At this stage of research, two processes of observation of data collection were conducted. The first process is to monitor water quality conditions on the user's smartphone display, in the form of the potential redox value of each pool measured by the ARMS instrument for 12 weeks. The second process is to compare the potential redox observation data measured by the ARMS instrument and the ORP Meter in each pool. Observation of comparative data took place during the 6th week. The sediment potential redox values for treatments 1, 2, and 3 were 69 mV, 151 mV, and 210 mV respectively. The average redox potentials in the water are in the range of 90.56 mV to -263 mV for treatment 1; 90.75 mV to -62,934 mV for treatment 2; 90.65 mV to 60.73 mV for treatment 3. This range is the measurement range from week 0 (shrimp seed stocking) to week 4. The results of the comparison of the two tools show that the accuracy of the ARMS tool is more than 95%.