With the worldwide industrialization of black soldier fly (BSF) production, it is necessary to better understand how the rearing scale and larvae density influence the performance of larvae and the quality of the final product. In this study, a factorial experiment was conducted to test the effect of rearing scale and density on the growth and composition of the BSF larvae. The larvae were grown in four different scales (box sizes), keeping the area and feed provided to each larva constant and in two different densities. The results reveal significant differences in the larval growth depending on the scale and density, which could be attributed to the higher temperatures achieved in the bigger scales with a temperature difference of more than 5 °C between the smallest and the biggest scale. Both the scale and the density influenced the composition of the larvae. The crude protein levels were higher on the smallest scale, and the lower density (ranging from 32.5% to 36.5%), and crude fat concentrations were the opposite (ranging from 31.7% to 20.1%). The density also influenced the concentrations of S, Mg, K, P, Fe, Zn, Cu, Al, B, and Co, in addition to the analyzed free amino acids PPS, ALA, CIT, and ANS. Furthermore, the rearing scale influenced the concentration of S, Zn, Cu, and Mo. The results provide further insight into the optimization of BSF production processes and the transfer of lab-scale results into big-scale production.

Larvae of Hermetia illucens, also commonly known as black soldier fly (BSF) have gained significant importance in the feed industry, primarily used as feed for aquaculture and other livestock farming. Mathematical models such as the Von Bertalanffy growth model and dynamic energy budget models are available for modelling the growth of various organisms but have their demerits for their application to the growth and development of BSF. Also, such dynamic models were not yet applied to the growth of the BSF larvae despite models proven to be useful for automation of industrial production process (e.g. feeding, heating/cooling, ventilation, harvesting, etc.). This work primarily focuses on developing a model based on the principles of the afore mentioned models from literature that can provide accurate mathematical description of the dry mass changes throughout the life cycle and the transition of development phases of the larvae. To further improve the accuracy of these models, various factors affecting the growth and development such as temperature, feed quality, feeding rate, moisture content in feed, and airflow rate are developed and integrated into the dynamic growth model. An extensive set of data was aggregated from various literature and used for the model development, parameter estimation and validation. Models describing the environmental factors were individually validated based on the data sets collected. In addition, the dynamic growth model was also validated for dry mass evolution and development stage transition of larvae reared on different substrate feeding rates. The developed models with the estimated parameters performed well, highlighting their potential application in decision-support systems and automation for large scale production.

Study and optimization of biological processes involved in food production require the quantitative study of the environment’s influences on the organism and eventually the products and byproducts consumed and produced. Commercial growth chambers do not fully support such quantitative study due to the underlying limitations (cost, size, resource exchange, programmability, user interaction, etc.). This work presents a low cost programmable system designed to facilitate such studies in organisms such as plants, fungi, and insect larvae. The proposed system consists of modular units performing specific functions. A sensor cluster for measuring gas concentrations, air properties (temperature, humidity, pressure), and growing medium properties was implemented and tested. Actuators for resource exchange, air conditioning, and light spectrum adjustment are proposed. A three-tier hierarchical software framework consisting of open-source cloud platform for data aggregation and user interaction; microcontroller firmware; and an application development framework for test automation and experiment regime design is developed. Series of experiments and tests were performed using the designed hardware and software to evaluate its capabilities and limitations. This controlled environment was used to study photosynthesis in Ocimum basilicum and, in a second experiment, the evolution of the metabolic activity of Hermetia illucens larvae over its larval phase.

Plant factories with artificial light are widely researched for food production in a controlled environment. For such control tasks, models of the energy and resource exchange in the production unit as well as those of the plant's growth process may be used. To achieve minimal operation cost, optimal control strategies can be applied to the system, taking into account the availability of resources by control reference specification. A particular advantage of model predictive control (MPC) is the incorporation of constraints that comply with actuator limitations and general plant growth conditions. In this work, a model of a production unit is derived including a description of the relation between the actuators' electrical signals and the input values to the model. Furthermore, a preliminary model based state tracking control is evaluated for production unit containing Lettuce. It could be observed that the controller is capable to track the reference while satisfying the constraint under changing weather conditions and resource availability.