Automation of Insect Mass Rearing and Processing Technologies of Mealworms (Tenebrio molitor) - Kröncke, Nina; Baur, Andreas; Böschen, Verena; Demtröder, Sebastian; Benning, Rainer; Delgado, Antonio (African Edible Insects As Alternative Source of Food, Oil, Protein and Bioactive Components, 2020)
Automating the production of insects for rearing, harvesting and processing is an important step in making insect mass production more attractive, cost effective and competitive. In this study, we present a functional model for the automated separation and monitoring of the breeding process of mealworms. Data have shown that it is possible to separate mealworm larvae from the substrate and faeces effectively with a zig-zag air separator and make handling and separating processes easier by automation. In addition, an integrated camera system with a neural network can assist in assessing the constitution of mealworms by using the segments of the larvae for evaluation. Regarding the processing of mealworm larvae, it has been shown that duration of drying and temperature have a significant influence on the nutritional value and quality of the larvae. In addition, the oil of the dried larvae was extracted with a screw press and the protein-rich press cake was processed with a rolling mill to insect meal, which is comparable to fishmeal and suitable for use in pet food and livestock fee
Keywords: Tenebrio molitor; Drying; Automation; Air classification; Image processing

Noninvasive monitoring system for Tenebrio molitor larvae based on image processing with a watershed algorithm and a neural net approach - Baur, Andreas; Koch, D.; Gatternig, B.; Delgado, Antonio (Journal of Insects as Food and Feed, 2022)
Due to the increase of the human world population, modern-day research is looking for new methods of protein exploitation. Therefore the authors conducted a joint research project with the goal to automate the breeding of Tenebrio molitor as a novel protein source. An important task is to monitor the size of larvae in order to control the rearing process. In this work, a suitable algorithm is presented to measure the size distribution of the population. It is a combination of classical image processing functions and a neural net to enhance the dataset for a more reliable result. The output can be used to determine the most efficient time for harvesting. First, a grayscale picture of the insects in one box is taken and binarised by a threshold algorithm. The connected objects in this image are separated by an irregular watershed algorithm that delivers separate segments of larvae. Not all single segments can be used for measuring the size distribution; therefore, an artificial neural network is used for a classification. In the end, the algorithm separates the segments given by the watershed and categorises them into four categories: good segments, medium segments, bad segments, and artefacts. The good segments have a recall rate of 91.4%. In the end, the identified segments can be used to establish a method for determining the size distribution and, thus, to document the growth of the larvae
CRIS-Team Scopus Importer:2022-09-02
Keywords: Automation; Insect rearing; Machine learning