Data-Driven Feed Optimization for Sustainable Aquaculture through Integrated Omics Analysis and Two-stage Bayesian optimization

Abstract

In aquaculture and livestock farming, developing feed that promotes efficient growth while minimizing environmental impact is crucial for sustainability. We developed a data-driven feed optimization system (DFOS) that integrates omics analysis with machine learning-based optimization technologies. First, DFOS employed Bayesian optimization (BO) to identify the optimal proportions of proteins and lipids, which are crucial feed components. Simultaneously, omics analysis was conducted to assess the physiological response of the targets to a given feed and identify key dietary components essential for growth. Next, BO was used to efficiently modify the combination and proportion of the specific additives identified in the first stage. We demonstrated the efficacy of DFOS by applying it to develop a new feed for the high-demand leopard coral grouper (Plectropomus leopardus) in Southeast Asia. This system provides an efficient data-driven framework for feed optimization across aquaculture and livestock, contributing significantly to more sustainable and productive farming practices.