Integration of IoT Sensors in Early Warning Systems for Mass Fish Deaths in Fish Farms

Abstract

Mass fish kill in aquaculture is a serious issue that can result in significant economic losses for farmers. One of the main causes of this occurrence is the changes in water quality parameters that are not detected early, such as a decrease in dissolved oxygen levels, an increase in ammonia, and extreme temperature fluctuations. This study aims to design and develop an Internet of Things (IoT) based early warning system that can monitor water quality parameters in real-time and provide automatic notifications when parameter values approach hazardous limits for fish. The system integrates various sensors such as temperature, pH, dissolved oxygen (DO), and ammonia sensors connected to a microcontroller and wireless communication module. The collected data is sent to a cloud platform for analysis and visualization through a web-based user interface or mobile application. The results of the system trials in the aquaculture environment show that this device is capable of detecting changes in water conditions quickly and accurately, and providing early notifications to breeders via text messages or app notifications. With this system, it is hoped that breeders can take rapid mitigation actions to prevent mass fish deaths, while also enhancing the efficiency and sustainability of aquaculture operations