Detecting Fish Movement Behavior based on Constant Temperature and pH using the IoT and Realtime Object Detection Model

This paper introduces a novel approach to water quality monitoring utilizing fish behavior as an indicator of aquatic environmental conditions. The proposed system integrates advanced technologies, including computer vision, sensors, and data analytics, to track and analyze the movement patterns of fish in real-time. Deployed sensors measure key water quality parameters such as temperature, pH, dissolved oxygen, and turbidity, while cameras capture fish activities within the monitored water bodies, then the livelihood of the fish will be affected. With problems that arise, solutions can be prepared by analyzing key components. Two components consist of a system that can automatically set fish feeding and the second component is water property measurement and level monitoring using the sensor module and electrical equipment with automatic video recording equipment installed, in observing the behavior and livelihood of aquatic animals. It has a communication connection in the form of the Internet of Things (IoT). We have developed a system that allows devices to measure temperature and pH in water and to display via the Internet for use in online data tracking and automatic installation of video recording equipment. By correlating observed fish behaviors with concurrently recorded water quality data, the system establishes meaningful relationships that enable the continuous assessment of the aquatic environment. From the results, it was known that the best conditions for the fish to survive. The project can be used as a prototype for creating an automatic fish tank that can adjust various parameters within the water if it is not appropriate for the well-being of aquatic animals. This innovative approach contributes to the advancement of environmental monitoring systems, fostering a deeper understanding of the dynamic interplay between aquatic ecosystems and water quality parameters.