Zoeken
Zoeken kan via de modus 'eenvoudig zoeken' (één veld) of uitgebreid via 'geavanceerd zoeken' (meerdere velden). Zo kan je bv. zoeken op een combinatie van een auteursnaam (auteur), een jaartal (jaar) en een documenttype.
Boekenmand
Nuttige resultaten kan je aanvinken en toevoegen aan een mandje. De inhoud hiervan kan je exporteren of afdrukken (naar bv. PDF).
RSS
Op de hoogte blijven van nieuw toegevoegde publicaties binnen uw interessegebied? Dit kan door een RSS-feed (?) te maken van jouw zoekopdracht.
nieuwe zoekopdracht
Vision-based egg quality prediction in Pacific bluefin tuna (Thunnus orientalis) by deep neural network
Ienaga, N.; Higuchi, K.; Takashi, T.; Gen, K.; Tsuda, K.; Terayama, K. (2021). Vision-based egg quality prediction in Pacific bluefin tuna (Thunnus orientalis) by deep neural network. NPG Scientific Reports 11(1): 6. https://dx.doi.org/10.1038/s41598-020-80001-0
In: Scientific Reports (Nature Publishing Group). Nature Publishing Group: London. ISSN 2045-2322; e-ISSN 2045-2322
|   |
| Auteurs | | Top |
- Ienaga, N.
- Higuchi, K.
- Takashi, T.
|
- Gen, K.
- Tsuda, K.
- Terayama, K.
|
|
| Abstract |
Closed-cycle aquaculture using hatchery produced seed stocks is vital to the sustainability of endangered species such as Pacific bluefin tuna (Thunnus orientalis) because this aquaculture system does not depend on aquaculture seeds collected from the wild. High egg quality promotes efficient aquaculture production by improving hatch rates and subsequent growth and survival of hatched larvae. In this study, we investigate the possibility of a simple, low-cost, and accurate egg quality prediction system based only on photographic images using deep neural networks. We photographed individual eggs immediately after spawning and assessed their qualities, i.e., whether they hatched normally and how many days larvae survived without feeding. The proposed system predicted normally hatching eggs with higher accuracy than human experts. It was also successful in predicting which eggs would produce longer-surviving larvae. We also analyzed the image aspects that contributed to the prediction to discover important egg features. Our results suggest the applicability of deep learning techniques to efficient egg quality prediction, and analysis of early developmental stages of development. |
IMIS is ontwikkeld en wordt gehost door het VLIZ.
