Deep Learning for Automated Egg Maturation Prediction of Atlantic Salmon Using Ultrasound Imaging

• Published in: IEEE Access
• Main Authors: Yasin Yari, Ingun Naeve, Per Helge Bergtun, Asle Hammerdal, Svein-Erik Masoy, Marco Marien Voormolen, Lasse Lovstakken
• Format: Article in Journal/Newspaper
• Language: English
• Published: IEEE 2024
• Subjects: Deep learning; ultrasound; maturation monitoring; salmon maturation; egg maturation prediction; Electrical engineering. Electronics. Nuclear engineering; TK1-9971; Atlantic salmon
• Online Access: https://doi.org/10.1109/ACCESS.2024.3409360; https://doaj.org/article/7f783abc97bd49289734623b53217765

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.