IoT-Based Automated Solution for Carp Fish Farming Using Sensor Networks and the Biochemical Parameters

Since fish, like many other living organisms, have a certain tolerance range for a variety of environmental factors, fish farming of particular types of fish species demands the fulfilment of a number of requirements. The people who work in the fish farming ponds must be active all day long to maintain the ecosystem for living fish. Therefore, the primary goal of this research is to monitor and take action to preserve the habitat's sustainable environment for carp fish species inside fishing ponds via distributed machine-to-machine communication, which will reduce the time required for some simple operations. Fish aquaculture, there are three major aquaculture types. The first one is marine aquaculture, the second one is brackish water aquaculture and the third one is Freshwater aquaculture also known as inland aquaculture. In this project, we mainly focused on problems that faced inland aquaculture. One of the major challenges of inland fish framing is maintaining the water quality to have healthy fish and get maximum output of fish- based products to local and foreign markets. As per NAQDA databases, the most popular inland farm fish is carp fish. So we chose carp fish as our main research object. The proposed system for fish feeding in aquaculture utilizes IoT technology to automate the feeding process according to the monitored behaviors of carp fish. The system uses sensors (Ph, Temperature, Turbidity) to gather data on environmental parameters, including water, temperature and fish feeding behaviors, which are then analyzed to improve feeding and overall fish health. By using the gathered sensor data, we will do the machine learning predictions and display the output using a React Native mobile application and a web-based dashboard.