AI-Driven Sustainable Ecosystem for Aquaculture with Nutrient Recycling

Aquaculture plays a crucial role in global food production, but maintaining optimal water quality in fish ponds

remains a major challenge. Traditional monitoring methods rely on manual inspection and periodic testing, which are timeconsuming and often fail to detect sudden changes in water conditions. This paper presents an AI-Driven Sustainable Ecosystem

for Aquaculture with Nutrient Recycling, an intelligent system that integrates Internet of Things (IoT), Machine Learning (ML),

and web technologies for real-time monitoring and automated control of aquatic environments. The system utilizes sensors to

continuously measure water quality parameters such as pH, turbidity, temperature, and total dissolved solids (TDS). Data is

processed using a Raspberry Pi controller that triggers automated responses such as UV disinfection and filtration when unsafe

conditions are detected. Additionally, a lightweight computer vision model using YOLO and TensorFlow Lite identifies floating

debris using a Raspberry Pi camera. The system also introduces a sustainable nutrient recycling module that converts fish waste

into organic fertilizer for plant irrigation. A web-based dashboard provides farmers with real-time monitoring, alerts, and

remote-control capabilities. The proposed system improves water quality management, reduces manual labour, and promotes

environmentally sustainable aquaculture practices.