Publication

Abstract : This study intends to improve sustainable agricultural practices by creating a cutting-edge smart aquaponics system that integrates real-time monitoring, Artificial Intelligence (AI) and the Internet of Things (IoT). The system offers real-time monitoring of vital environmental parameters, such as Total Dissolved Solids (TDS), pH, temperature, water level, water contact, air gap and soil moisture, using an ESP32-CAM module and multiple Internet of Things sensors. This information is gathered by the Micro Python-based ESP32 microcontroller and transmitted to the ThingsBoard Cloud through secure HTTP requests that use JWT authentication. Early identification of possible risks using machine learning algorithms like Isolation Forest, which identify anomalies in variables like pH, TDS and moisture content, enables proactive fertilizer and irrigation management adjustments. Additionally, a Telegram bot was developed to incorporate an ESP32-CAM module for remote visual monitoring in real time. This allows users to receive system updates and real-time photos of plant growth directly on their mobile devices. Fish feeding, water flow and aeration are all automated with the help of the microcontroller's built-in PID control algorithm, guaranteeing ideal environmental conditions with little assistance from humans. Real-time data retrieved from the ThingsBoard dashboard is interactively visualized using a web-based demo application created with Streamlit. Based on Twilio, it offers automated SMS warnings for critical situations, live charting and anomaly detection. This smart aquaponics system greatly improves efficiency, scalability and sustainability by combining IoT, AI-driven anomaly detection, camera monitoring and automated control.