Water Quality and Environment Suitability Feedback System for Local Fish Farming: A Fuzzy Logic-Based Approach

In Bangladesh, traditional fish farming practices face challenges due to reliance on manual monitoring and delayed responses to changes in water quality, leading to adverse impacts on fish health and farmers’ economic outcomes. To address these issues, a project has been developed that integrates fuzzy logic controllers for real-time monitoring of critical water parameters, including temperature, pH, dissolved oxygen, electrical conductivity, turbidity, and total dissolved solids. This innovative system employs intelligent fuzzification techniques to translate sensor data into linguistic categories, facilitating more nuanced decision-making compared to conventional binary systems. It also allows for species-specific environmental management for local fish such as Carp, Catfish, Koi, and Tilapia. Using a fuzzy inference engine to automatically regulate aeration systems in response to variable water conditions, the system ensures optimal dissolved oxygen levels while lowering energy usage, making it particularly beneficial for off-grid and rural areas with limited power access. In addition, a user-friendly Android application enables farmers to receive alerts, access real-time water quality data, and remotely control aerators.