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Multipurpose monitoring system for edible insect breeding based on machine learning
The Tenebrio molitor has become the first insect added to the catalogue of novel foods by the European Food Safety Authority due to its rich nutritional value and the low carbon footprint produced during its breeding. The large scale of Tenebrio molitor breeding makes automation of the process, whic...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9098436/ https://www.ncbi.nlm.nih.gov/pubmed/35551215 http://dx.doi.org/10.1038/s41598-022-11794-5 |
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author | Majewski, Paweł Zapotoczny, Piotr Lampa, Piotr Burduk, Robert Reiner, Jacek |
author_facet | Majewski, Paweł Zapotoczny, Piotr Lampa, Piotr Burduk, Robert Reiner, Jacek |
author_sort | Majewski, Paweł |
collection | PubMed |
description | The Tenebrio molitor has become the first insect added to the catalogue of novel foods by the European Food Safety Authority due to its rich nutritional value and the low carbon footprint produced during its breeding. The large scale of Tenebrio molitor breeding makes automation of the process, which is supported by a monitoring system, essential. Present research involves the development of a 3-module system for monitoring Tenebrio molitor breeding. The instance segmentation module (ISM) detected individual growth stages (larvae, pupae, beetles) of Tenebrio molitor, and also identified anomalies: dead larvae and pests. The semantic segmentation module (SSM) extracted feed, chitin, and frass from the obtained image. The larvae phenotyping module (LPM) calculated features for both individual larvae (length, curvature, mass, division into segments, and their classification) and the whole population (length distribution). The modules were developed using machine learning models (Mask R-CNN, U-Net, LDA), and were validated on different samples of real data. Synthetic image generation using a collection of labelled objects was used, which significantly reduced the development time of the models and reduced the problems of dense scenes and the imbalance of the considered classes. The obtained results (average [Formula: see text] for ISM and average [Formula: see text] for SSM) confirm the great potential of the proposed system. |
format | Online Article Text |
id | pubmed-9098436 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-90984362022-05-14 Multipurpose monitoring system for edible insect breeding based on machine learning Majewski, Paweł Zapotoczny, Piotr Lampa, Piotr Burduk, Robert Reiner, Jacek Sci Rep Article The Tenebrio molitor has become the first insect added to the catalogue of novel foods by the European Food Safety Authority due to its rich nutritional value and the low carbon footprint produced during its breeding. The large scale of Tenebrio molitor breeding makes automation of the process, which is supported by a monitoring system, essential. Present research involves the development of a 3-module system for monitoring Tenebrio molitor breeding. The instance segmentation module (ISM) detected individual growth stages (larvae, pupae, beetles) of Tenebrio molitor, and also identified anomalies: dead larvae and pests. The semantic segmentation module (SSM) extracted feed, chitin, and frass from the obtained image. The larvae phenotyping module (LPM) calculated features for both individual larvae (length, curvature, mass, division into segments, and their classification) and the whole population (length distribution). The modules were developed using machine learning models (Mask R-CNN, U-Net, LDA), and were validated on different samples of real data. Synthetic image generation using a collection of labelled objects was used, which significantly reduced the development time of the models and reduced the problems of dense scenes and the imbalance of the considered classes. The obtained results (average [Formula: see text] for ISM and average [Formula: see text] for SSM) confirm the great potential of the proposed system. Nature Publishing Group UK 2022-05-12 /pmc/articles/PMC9098436/ /pubmed/35551215 http://dx.doi.org/10.1038/s41598-022-11794-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Majewski, Paweł Zapotoczny, Piotr Lampa, Piotr Burduk, Robert Reiner, Jacek Multipurpose monitoring system for edible insect breeding based on machine learning |
title | Multipurpose monitoring system for edible insect breeding based on machine learning |
title_full | Multipurpose monitoring system for edible insect breeding based on machine learning |
title_fullStr | Multipurpose monitoring system for edible insect breeding based on machine learning |
title_full_unstemmed | Multipurpose monitoring system for edible insect breeding based on machine learning |
title_short | Multipurpose monitoring system for edible insect breeding based on machine learning |
title_sort | multipurpose monitoring system for edible insect breeding based on machine learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9098436/ https://www.ncbi.nlm.nih.gov/pubmed/35551215 http://dx.doi.org/10.1038/s41598-022-11794-5 |
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