Abstract:
The increasing number of fish farms worldwide faces many problems, such as labor
shortage, high operational cost, difficulty in monitoring and maintenance, water
contamination, high acidity levels, fish illness, and high population density. The main
objective of this study is to design and implement a smart, real-time water quality
monitoring and management system for aquaculture using Internet of Things (IoT) and
Artificial Intelligence (AI) technologies. The system integrates pH, turbidity, underwater
ultrasonic, and temperature sensors connected to an ESP32 microcontroller programmed
using C++ and ESP-IDF. A design-build-test approach was adopted, where sensor
calibration, data acquisition, and cloud-based analysis were performed at hourly interval
to validate system performance. Data were transmitted via SIM800L and GPS modules
to Google Sheets and Firebase for real-time visualization and storage, and the system was
linked to both a webpage dashboard and mobile application for remote monitoring.
Experimental evaluation demonstrated reliable wireless communication, accurate sensor
readings within ±2% error, data uploading within 500 ms, and stable cloud
synchronization. AI and ML algorithms were applied to analyze data, detect anomalies,
predict water quality variations, and recommend corrective measures. This solution
introduces a modular underwater monitoring platform with wireless and wired
communication, enhancing automation, scalability, and sustainability in aquaculture
management. Overall, the project effectively minimizes labor, cost, and time while
ensuring maximum yield and a healthier aquatic environment.
