SMART SOLAR-POWERED AQUAPONICS SYSTEM FOR SUSTAINABLE FOOD PRODUCTION

Abstract

Household-scale aquaponic systems face three principal problems: dependence on the conventional electricity grid, the need for intensive manual monitoring, and high technical-skill requirements. This research aims to design, build, and test a smart aquaponic system that integrates Internet of Things (IoT) technology for automation with an off-grid Solar Power Plant (PV system) to achieve energy independence. The approach employed is the Research and Development (R&D) method using the 4-D model. The system was built using an ESP32-C3 microcontroller, an E-201-C pH sensor, a DS18B20 temperature sensor, and an RTC to control the circulation pump (via a relay) and an automatic feeder (SG90 servo); power is supplied by a 100 Wp solar panel, a 12V 100Ah VRLA battery, and a 20A PWM solar charge controller. Data communication uses the MQTT protocol with a Node.js–MySQL backend and a React.js-based Smart Aquaponics web application featuring an interactive two-point pH calibration. Key findings show that the pH sensor achieved 99.57% accuracy (mean absolute error 0.028) and the temperature sensor had an average deviation of 0.2°C; the pump and feeder actuators operated on schedule with maximum deviations of 2 and 1 seconds, respectively; all black-box functional tests passed (100%) with response times under two seconds; and the PV system independently met the daily energy demand of 272 Wh over seven days with 99.86% uptime and 99.79% data transmission success. One limitation identified is the relatively long stabilization time of the E 201-C pH sensor (approximately 15–30 minutes), so an upgrade to a more reliable, accurate, and precise industrial or laboratory-grade pH sensor is recommended. It is concluded that all research objectives were achieved and that combining smart farming, IoT, and renewable energy into a single functional prototype constitutes a sustainable, land-efficient, water-efficient, and energy-independent food production solution. The implications are that the system reduces the manual monitoring burden, removes dependence on the electricity grid so it can be deployed in remote locations, lowers the technical-skill barrier, and offers a replicable model for strengthening household food security.