Deep Learning for Automated Egg Maturation Prediction of Atlantic Salmon using Ultrasound Imaging
The Atlantic salmon maturation process has been studied for decades to increase the quantity and quality of the production in farming facilities. An important topic in this context is the salmon egg maturation process. Ultrasound imaging is considered an effective tool for monitoring the egg develop...
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Institute of Electrical and Electronics Engineers (IEEE)
2024
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Online Access: | http://dx.doi.org/10.36227/techrxiv.171504549.93473450/v2 |
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crieeecr:10.36227/techrxiv.171504549.93473450/v2 2024-09-09T19:30:15+00:00 Deep Learning for Automated Egg Maturation Prediction of Atlantic Salmon using Ultrasound Imaging Yari, Yasin Naeve, Ingun Bergtun, Per Helge Hammerdal, Asle Måsøy, Svein-Erik Voormolen, Marco Marien Lovstakken, Lasse 2024 http://dx.doi.org/10.36227/techrxiv.171504549.93473450/v2 unknown Institute of Electrical and Electronics Engineers (IEEE) https://creativecommons.org/licenses/by/4.0/ posted-content 2024 crieeecr https://doi.org/10.36227/techrxiv.171504549.93473450/v2 2024-06-20T04:12:33Z The Atlantic salmon maturation process has been studied for decades to increase the quantity and quality of the production in farming facilities. An important topic in this context is the salmon egg maturation process. Ultrasound imaging is considered an effective tool for monitoring the egg development stage of salmon, but manual inspection is time-consuming and dependent on operator experience. We propose a method for automated monitoring of the egg maturation stage in salmon using deep learning, providing complimentary decisions on egg morphology. A segmentation network was developed to solve the challenge of separating and measuring individual eggs in the ovary. The segmentation part was combined with a classification network to determine the maturation stage of the eggs. Our model was able to segment eggs and classify their development stage with over 88% accuracy, outperforming established methods designed for similar tasks. A real-time application was developed which provided an estimation of size and maturity stage while scanning. The egg state estimation showed potential for replacing manual evaluations and can enable fully automatic evaluation of maturation in Atlantic salmon. Other/Unknown Material Atlantic salmon IEEE Publications |
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IEEE Publications |
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The Atlantic salmon maturation process has been studied for decades to increase the quantity and quality of the production in farming facilities. An important topic in this context is the salmon egg maturation process. Ultrasound imaging is considered an effective tool for monitoring the egg development stage of salmon, but manual inspection is time-consuming and dependent on operator experience. We propose a method for automated monitoring of the egg maturation stage in salmon using deep learning, providing complimentary decisions on egg morphology. A segmentation network was developed to solve the challenge of separating and measuring individual eggs in the ovary. The segmentation part was combined with a classification network to determine the maturation stage of the eggs. Our model was able to segment eggs and classify their development stage with over 88% accuracy, outperforming established methods designed for similar tasks. A real-time application was developed which provided an estimation of size and maturity stage while scanning. The egg state estimation showed potential for replacing manual evaluations and can enable fully automatic evaluation of maturation in Atlantic salmon. |
format |
Other/Unknown Material |
author |
Yari, Yasin Naeve, Ingun Bergtun, Per Helge Hammerdal, Asle Måsøy, Svein-Erik Voormolen, Marco Marien Lovstakken, Lasse |
spellingShingle |
Yari, Yasin Naeve, Ingun Bergtun, Per Helge Hammerdal, Asle Måsøy, Svein-Erik Voormolen, Marco Marien Lovstakken, Lasse Deep Learning for Automated Egg Maturation Prediction of Atlantic Salmon using Ultrasound Imaging |
author_facet |
Yari, Yasin Naeve, Ingun Bergtun, Per Helge Hammerdal, Asle Måsøy, Svein-Erik Voormolen, Marco Marien Lovstakken, Lasse |
author_sort |
Yari, Yasin |
title |
Deep Learning for Automated Egg Maturation Prediction of Atlantic Salmon using Ultrasound Imaging |
title_short |
Deep Learning for Automated Egg Maturation Prediction of Atlantic Salmon using Ultrasound Imaging |
title_full |
Deep Learning for Automated Egg Maturation Prediction of Atlantic Salmon using Ultrasound Imaging |
title_fullStr |
Deep Learning for Automated Egg Maturation Prediction of Atlantic Salmon using Ultrasound Imaging |
title_full_unstemmed |
Deep Learning for Automated Egg Maturation Prediction of Atlantic Salmon using Ultrasound Imaging |
title_sort |
deep learning for automated egg maturation prediction of atlantic salmon using ultrasound imaging |
publisher |
Institute of Electrical and Electronics Engineers (IEEE) |
publishDate |
2024 |
url |
http://dx.doi.org/10.36227/techrxiv.171504549.93473450/v2 |
genre |
Atlantic salmon |
genre_facet |
Atlantic salmon |
op_rights |
https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.36227/techrxiv.171504549.93473450/v2 |
_version_ |
1809899249665572864 |