Vision-based egg quality prediction in Pacific bluefin tuna (Thunnus orientalis) by deep neural network

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 efficie...

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Autores principales:	Naoto Ienaga, Kentaro Higuchi, Toshinori Takashi, Koichiro Gen, Koji Tsuda, Kei Terayama
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/3a102b68b9774ab5b04173b3d1c247c0
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spelling	oai:doaj.org-article:3a102b68b9774ab5b04173b3d1c247c02021-12-02T14:01:36ZVision-based egg quality prediction in Pacific bluefin tuna (Thunnus orientalis) by deep neural network10.1038/s41598-020-80001-02045-2322https://doaj.org/article/3a102b68b9774ab5b04173b3d1c247c02021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80001-0https://doaj.org/toc/2045-2322Abstract 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.Naoto IenagaKentaro HiguchiToshinori TakashiKoichiro GenKoji TsudaKei TerayamaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Naoto Ienaga Kentaro Higuchi Toshinori Takashi Koichiro Gen Koji Tsuda Kei Terayama Vision-based egg quality prediction in Pacific bluefin tuna (Thunnus orientalis) by deep neural network
description	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.
format	article
author	Naoto Ienaga Kentaro Higuchi Toshinori Takashi Koichiro Gen Koji Tsuda Kei Terayama
author_facet	Naoto Ienaga Kentaro Higuchi Toshinori Takashi Koichiro Gen Koji Tsuda Kei Terayama
author_sort	Naoto Ienaga
title	Vision-based egg quality prediction in Pacific bluefin tuna (Thunnus orientalis) by deep neural network
title_short	Vision-based egg quality prediction in Pacific bluefin tuna (Thunnus orientalis) by deep neural network
title_full	Vision-based egg quality prediction in Pacific bluefin tuna (Thunnus orientalis) by deep neural network
title_fullStr	Vision-based egg quality prediction in Pacific bluefin tuna (Thunnus orientalis) by deep neural network
title_full_unstemmed	Vision-based egg quality prediction in Pacific bluefin tuna (Thunnus orientalis) by deep neural network
title_sort	vision-based egg quality prediction in pacific bluefin tuna (thunnus orientalis) by deep neural network
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/3a102b68b9774ab5b04173b3d1c247c0
work_keys_str_mv	AT naotoienaga visionbasedeggqualitypredictioninpacificbluefintunathunnusorientalisbydeepneuralnetwork AT kentarohiguchi visionbasedeggqualitypredictioninpacificbluefintunathunnusorientalisbydeepneuralnetwork AT toshinoritakashi visionbasedeggqualitypredictioninpacificbluefintunathunnusorientalisbydeepneuralnetwork AT koichirogen visionbasedeggqualitypredictioninpacificbluefintunathunnusorientalisbydeepneuralnetwork AT kojitsuda visionbasedeggqualitypredictioninpacificbluefintunathunnusorientalisbydeepneuralnetwork AT keiterayama visionbasedeggqualitypredictioninpacificbluefintunathunnusorientalisbydeepneuralnetwork
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Vision-based egg quality prediction in Pacific bluefin tuna (Thunnus orientalis) by deep neural network

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