ONLYFINS: Aerator Automated Android-Based Aquaculture Monitoring System Using Computer Vision for CLSU-FAC’s Catfish Farming
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Date
2024-03-13
Authors
Iñigo, Ivan Aldrich D.
Ramos, Jayne Vernice C.
Ventanilla, Luiz Dwayne C.
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Abstract
In the Philippines, the adoption of 'smart' technologies such as the Internet of Things (IoT) remains limited in local aquaculture, particularly in catfish farming—a sector endorsed by the government for backyard cultivation. While catfish are generally perceived as resilient, it is critical for fish farmers to prioritize optimal pond aeration. Neglecting this aspect can lead to issues including stunted growth, low survival rates, cannibalism, and increased mortality, all of which threaten the current setup of catfish ponds at Central Luzon State University’s Freshwater Aquaculture Center. To address these concerns, the objectives of this study were to develop a system for automatically monitoring gasping catfish, create a cost-effective computer vision-based monitoring system capable of real-time analysis to detect gasping behavior, automate monitoring processes to alert farmers of aeration issues, and integrate automated aerators with manual overrides. The Rapid Application Development (RAD) methodology—comprising Requirements Planning, User Design, Construction, and Cutover phases—was employed to streamline development, lower costs, reduce time to market, and facilitate early system integration. Utilizing an ESP32-Camera Module to run YOLOv8 live inference on the CLSU-FAC’s catfish pond, the system maintains optimal aeration by automating the aerator via a Relay Module and WiFi module connected to an Arduino. This automation reduces labor requirements and eliminates the need for constant physical monitoring and manual intervention. The integration of real-time data collection, sensor technology, and automated aeration has proven effective in preserving fish health and improving operational efficiency in aquaculture facilities.