Viability and continuity are the key pillars toward building a sustainable path, but not the only ones to achieving it. In order to create environmentally and economically green and renewable insect farms, sustainable insect diets have to be enhanced.
The CoRoSect team has taken this issue under its roof. They have designed the experiment from which the information on developing a decision support tool for insect rearing and on insect feeding will be extracted. Preferred rearing conditions and parameters are the key factors to be followed, and considered insects are mealworms, black soldier flies and house crickets. These factors aim to find out the most optimal diet for these insects, hence enabling sustainable insect farming.
The method to conduct this experiment is through the inclusion of different side streams of agriculture and food sector in the diets, the nutritional content of diets, and the supply of water to insects.
In one of many presentations, it was highlighted that various types of local side streams from the food industry are currently utilized as substrates in black soldier fly larvae (BSFL) production. To meet high nutritional quality, the substrate is often designed by standardizing carbohydrate: protein ratio. For larvae growth and well-being, it would also be important to control the content of certain amino acids, while planning the composition of the substrate.
CoRoSect is developing a novel Cognitive Robotic System for Digitalized and Networked (Automated) Insect Farms. We bring leading-edge robotics, AI, and some of the best experts in our industry - to help embrace automation and wave goodbye to the monotonous and mundane tasks.
Dr. Rico Möckel
Maastricht University
Department of Data Science and Knowledge Engineering (DKE)
Paul Henri Spaaklaan 1
6229EN Maastricht
The Netherlands
Tel.: +31433883482
rico.mockel@maastrichtuniversity.nl
Prof. Dr. Mladen Radišić
CEO Foodscale Hub
Narodnog fronta 73
21000 Novi Sad
Serbia
Tel.: (+381) 21 300 8023
mladen@foodscalehub.com
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101016953.