Dry biomass estimation in production of insects larvae using Interconnected Generalized Super-Twisting Observer

Alternative protein sources are becoming essential for achieving a sustainable food system. The Black Soldier Fly larvae (BSFL), a protein-rich insect capable of feeding on a wide range of organic materials, shows immense potential for use in bio-conversion. It is already being used in poultry and fish aquaculture and is currently under evaluation for human consumption. Consequently, the farming of this insect is of great interest, and advanced control methods could significantly optimize the process and improve resource efficiency. One of the main challenges in applying these advanced techniques is the lack of information about certain critical system states, particularly the estimation of dry biomass weight. Measuring the dry biomass weight of the larvae is a destructive process that can only be performed at the beginning and end of the cycle. This low sampling frequency is insufficient for the application of advanced control strategies. Thus, a non-invasive estimation method is required. This work addresses the observer design problem for estimating the dry biomass weight of BSFL. The objective is to obtain an online estimation of this weight before the larvae reach maturity. To achieve this, a reduced version of the existing BSFL full-fledged model is proposed, based on specific assumptions. A subsystem is extracted from this BSFL reduced model, for which, a necessary and sufficient condition is provided for its global strong observability. Moreover, an interconnection of Generalized Super-Twisting Observers is designed, and a comparison is made between this method and the high-gain observer. • Application relevant model of insects larvae growth model suitable for estimation. • Extraction of a subsystem for the dry biomass estimation. • Sufficient and necessary condition for strong observability of the dry biomass. • Transformation to Interconnected Generalized Super-Twisting Observer form (IGSTO). • Application of the IGSTO using data obtained from full-fledged model.