Fish Guard: A Holistic Approach to Automated Fish Farming with IoT and Image Processing

Abstract

The ornamental fish industry, which is important to the global economy, faces challenges that hinder its productivity and sustainability, particularly feeding practices, water quality management and disease control. This research introduces an integrated system designed to automate and optimize these aspects using the ESP32 microcontroller. Offers a lower cost and more effective, low-power solution with real-time processing capabilities. The proposed system has developed along four main lines. An automated feeding system that adapts to the food needs of fish, a water quality management system that monitors and controls critical parameters through machine learning algorithms, and a disease management system that uses image processing techniques for early detection. The systems were tested in controlled environments, showing significant improvements in nutrient efficiency, water quality stability and disease prevention. Our findings suggest that the integration of these technologies can significantly enhance the operational efficiency and sustainability of ornamental fish farms.