KUL06: An AI-Driven Precision Feeding for Sustainable Aquaculture

The global consumption of fish, as a vital source of protein, has doubled from the 1960s to 2020s. Since the 1990s, captured fisheries have plateaued while demand continues to grow, with aquaculture stepping in to meet the gap. However, feed costs account for 65–70% of a fish farm’s operational expenses. Traditional feeding practices rely on observation, experience, and trial and error, making them error-prone and inefficient.
This lack of precision often leads to overfeeding or underfeeding, both of which carry serious consequences. Overfeeding results in feed wastage and deterioration water quality, while underfeeding stunts fish growth. For example, Egypt’s aquaculture sector alone loses an estimated 480,000 tonnes of feed annually—equivalent to around $240 million in lost input costs.
This challenge seeks to develop an AI-powered web application that supports precision feeding in aquaculture by analysing real-time data to guide feeding decisions. The goal is to help fish farm reduce feed waste, improve animal welfare, and increase farm profitability through smarter, data-driven practices.

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