A machine-learning approach to optimize nutritional properties and organic wastes recycling efficiency conversed by black soldier fly (Hermetia illucens)

Abstract

Suboptimal nutrition in organic waste limits the growth of black soldier fly (BSF) larvae, thereby reducing biowaste recycling efficiency. In this study, weight gain data from BSF larvae fed diets with distinct nutrient compositions were used to build a machine learning model. Among the algorithms tested, the XGBoost model demonstrated the best performance in predicting weight gain. The model identified protein as the most critical nutrient factor for larval biomass and was used to determine the optimal diet by calculating the highest weight gain from 30,000 randomly generated nutrient combinations. Supplementing the missing nutrients in organic waste according to the optimal diet improved the weight gain and feed conversion rate of BSF larvae. Feeding larvae a mixture of organic wastes, a cost-effective strategy to meet dietary nutrition requirements, resulted in significant increases in both the bioconversion rate (up to 9.7%) and mass reduction rate (up to 22.8%) of organic waste.