Normal hatching rate estimation for bulk samples of Pacific bluefin tuna (

In the aquaculture of Pacific bluefin tuna (PBT, Thunnus orientalis), low survival rates during the larval stage remain a significant issue for the stable supply of seedlings reared from eggs. Egg quality is an important factor affecting seedling production success. If egg quality can be evaluated b...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Ienaga, Naoto [verfasserIn] Higuchi, Kentaro [verfasserIn] Takashi, Toshinori [verfasserIn] Gen, Koichiro [verfasserIn] Terayama, Kei [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Seedling production Egg quality Deep learning Convolutional neural network

Übergeordnetes Werk:	Enthalten in: Aquacultural engineering - Amsterdam [u.a.] : Elsevier Science, 1982, 98
Übergeordnetes Werk:	volume:98

DOI / URN:	10.1016/j.aquaeng.2022.102274

Katalog-ID:	ELV058488332

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520			\|a In the aquaculture of Pacific bluefin tuna (PBT, Thunnus orientalis), low survival rates during the larval stage remain a significant issue for the stable supply of seedlings reared from eggs. Egg quality is an important factor affecting seedling production success. If egg quality can be evaluated before hatching, the efficiency of seedling production will be improved by selectively cultivating the seedlings with a high survival rate. In our previous study, we developed a system using a convolutional neural network to evaluate PBT egg quality by estimating the normal hatching rate. The system used an image containing one PBT egg as the input. To further improve the efficiency of the egg quality estimation system, we updated the system that estimates the egg quality of a bulk sample (approximately 30 eggs). Our results indicated that the proposed system can estimate the normal hatching rate of the bulk sample with higher accuracy than the visual inspections of three field experts and visualize the normal hatching rate of each egg in the bulk sample. The proposed system will serve as a foundation for assessing the quality of PBT eggs and increasing the efficiency of seedling production.
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allfields	10.1016/j.aquaeng.2022.102274 doi (DE-627)ELV058488332 (ELSEVIER)S0144-8609(22)00050-4 DE-627 ger DE-627 rda eng 550 690 VZ 48.68 bkl Ienaga, Naoto verfasserin aut Normal hatching rate estimation for bulk samples of Pacific bluefin tuna ( 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the aquaculture of Pacific bluefin tuna (PBT, Thunnus orientalis), low survival rates during the larval stage remain a significant issue for the stable supply of seedlings reared from eggs. Egg quality is an important factor affecting seedling production success. If egg quality can be evaluated before hatching, the efficiency of seedling production will be improved by selectively cultivating the seedlings with a high survival rate. In our previous study, we developed a system using a convolutional neural network to evaluate PBT egg quality by estimating the normal hatching rate. The system used an image containing one PBT egg as the input. To further improve the efficiency of the egg quality estimation system, we updated the system that estimates the egg quality of a bulk sample (approximately 30 eggs). Our results indicated that the proposed system can estimate the normal hatching rate of the bulk sample with higher accuracy than the visual inspections of three field experts and visualize the normal hatching rate of each egg in the bulk sample. The proposed system will serve as a foundation for assessing the quality of PBT eggs and increasing the efficiency of seedling production. Seedling production Egg quality Deep learning Convolutional neural network Higuchi, Kentaro verfasserin aut Takashi, Toshinori verfasserin aut Gen, Koichiro verfasserin aut Terayama, Kei verfasserin aut Enthalten in Aquacultural engineering Amsterdam [u.a.] : Elsevier Science, 1982 98 Online-Ressource (DE-627)306313960 (DE-600)1495995-1 (DE-576)256146810 0144-8609 nnns volume:98 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.68 Aquakultur VZ AR 98
spelling	10.1016/j.aquaeng.2022.102274 doi (DE-627)ELV058488332 (ELSEVIER)S0144-8609(22)00050-4 DE-627 ger DE-627 rda eng 550 690 VZ 48.68 bkl Ienaga, Naoto verfasserin aut Normal hatching rate estimation for bulk samples of Pacific bluefin tuna ( 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the aquaculture of Pacific bluefin tuna (PBT, Thunnus orientalis), low survival rates during the larval stage remain a significant issue for the stable supply of seedlings reared from eggs. Egg quality is an important factor affecting seedling production success. If egg quality can be evaluated before hatching, the efficiency of seedling production will be improved by selectively cultivating the seedlings with a high survival rate. In our previous study, we developed a system using a convolutional neural network to evaluate PBT egg quality by estimating the normal hatching rate. The system used an image containing one PBT egg as the input. To further improve the efficiency of the egg quality estimation system, we updated the system that estimates the egg quality of a bulk sample (approximately 30 eggs). Our results indicated that the proposed system can estimate the normal hatching rate of the bulk sample with higher accuracy than the visual inspections of three field experts and visualize the normal hatching rate of each egg in the bulk sample. The proposed system will serve as a foundation for assessing the quality of PBT eggs and increasing the efficiency of seedling production. Seedling production Egg quality Deep learning Convolutional neural network Higuchi, Kentaro verfasserin aut Takashi, Toshinori verfasserin aut Gen, Koichiro verfasserin aut Terayama, Kei verfasserin aut Enthalten in Aquacultural engineering Amsterdam [u.a.] : Elsevier Science, 1982 98 Online-Ressource (DE-627)306313960 (DE-600)1495995-1 (DE-576)256146810 0144-8609 nnns volume:98 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.68 Aquakultur VZ AR 98
allfields_unstemmed	10.1016/j.aquaeng.2022.102274 doi (DE-627)ELV058488332 (ELSEVIER)S0144-8609(22)00050-4 DE-627 ger DE-627 rda eng 550 690 VZ 48.68 bkl Ienaga, Naoto verfasserin aut Normal hatching rate estimation for bulk samples of Pacific bluefin tuna ( 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the aquaculture of Pacific bluefin tuna (PBT, Thunnus orientalis), low survival rates during the larval stage remain a significant issue for the stable supply of seedlings reared from eggs. Egg quality is an important factor affecting seedling production success. If egg quality can be evaluated before hatching, the efficiency of seedling production will be improved by selectively cultivating the seedlings with a high survival rate. In our previous study, we developed a system using a convolutional neural network to evaluate PBT egg quality by estimating the normal hatching rate. The system used an image containing one PBT egg as the input. To further improve the efficiency of the egg quality estimation system, we updated the system that estimates the egg quality of a bulk sample (approximately 30 eggs). Our results indicated that the proposed system can estimate the normal hatching rate of the bulk sample with higher accuracy than the visual inspections of three field experts and visualize the normal hatching rate of each egg in the bulk sample. The proposed system will serve as a foundation for assessing the quality of PBT eggs and increasing the efficiency of seedling production. Seedling production Egg quality Deep learning Convolutional neural network Higuchi, Kentaro verfasserin aut Takashi, Toshinori verfasserin aut Gen, Koichiro verfasserin aut Terayama, Kei verfasserin aut Enthalten in Aquacultural engineering Amsterdam [u.a.] : Elsevier Science, 1982 98 Online-Ressource (DE-627)306313960 (DE-600)1495995-1 (DE-576)256146810 0144-8609 nnns volume:98 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.68 Aquakultur VZ AR 98
allfieldsGer	10.1016/j.aquaeng.2022.102274 doi (DE-627)ELV058488332 (ELSEVIER)S0144-8609(22)00050-4 DE-627 ger DE-627 rda eng 550 690 VZ 48.68 bkl Ienaga, Naoto verfasserin aut Normal hatching rate estimation for bulk samples of Pacific bluefin tuna ( 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the aquaculture of Pacific bluefin tuna (PBT, Thunnus orientalis), low survival rates during the larval stage remain a significant issue for the stable supply of seedlings reared from eggs. Egg quality is an important factor affecting seedling production success. If egg quality can be evaluated before hatching, the efficiency of seedling production will be improved by selectively cultivating the seedlings with a high survival rate. In our previous study, we developed a system using a convolutional neural network to evaluate PBT egg quality by estimating the normal hatching rate. The system used an image containing one PBT egg as the input. To further improve the efficiency of the egg quality estimation system, we updated the system that estimates the egg quality of a bulk sample (approximately 30 eggs). Our results indicated that the proposed system can estimate the normal hatching rate of the bulk sample with higher accuracy than the visual inspections of three field experts and visualize the normal hatching rate of each egg in the bulk sample. The proposed system will serve as a foundation for assessing the quality of PBT eggs and increasing the efficiency of seedling production. Seedling production Egg quality Deep learning Convolutional neural network Higuchi, Kentaro verfasserin aut Takashi, Toshinori verfasserin aut Gen, Koichiro verfasserin aut Terayama, Kei verfasserin aut Enthalten in Aquacultural engineering Amsterdam [u.a.] : Elsevier Science, 1982 98 Online-Ressource (DE-627)306313960 (DE-600)1495995-1 (DE-576)256146810 0144-8609 nnns volume:98 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.68 Aquakultur VZ AR 98
allfieldsSound	10.1016/j.aquaeng.2022.102274 doi (DE-627)ELV058488332 (ELSEVIER)S0144-8609(22)00050-4 DE-627 ger DE-627 rda eng 550 690 VZ 48.68 bkl Ienaga, Naoto verfasserin aut Normal hatching rate estimation for bulk samples of Pacific bluefin tuna ( 2022 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier In the aquaculture of Pacific bluefin tuna (PBT, Thunnus orientalis), low survival rates during the larval stage remain a significant issue for the stable supply of seedlings reared from eggs. Egg quality is an important factor affecting seedling production success. If egg quality can be evaluated before hatching, the efficiency of seedling production will be improved by selectively cultivating the seedlings with a high survival rate. In our previous study, we developed a system using a convolutional neural network to evaluate PBT egg quality by estimating the normal hatching rate. The system used an image containing one PBT egg as the input. To further improve the efficiency of the egg quality estimation system, we updated the system that estimates the egg quality of a bulk sample (approximately 30 eggs). Our results indicated that the proposed system can estimate the normal hatching rate of the bulk sample with higher accuracy than the visual inspections of three field experts and visualize the normal hatching rate of each egg in the bulk sample. The proposed system will serve as a foundation for assessing the quality of PBT eggs and increasing the efficiency of seedling production. Seedling production Egg quality Deep learning Convolutional neural network Higuchi, Kentaro verfasserin aut Takashi, Toshinori verfasserin aut Gen, Koichiro verfasserin aut Terayama, Kei verfasserin aut Enthalten in Aquacultural engineering Amsterdam [u.a.] : Elsevier Science, 1982 98 Online-Ressource (DE-627)306313960 (DE-600)1495995-1 (DE-576)256146810 0144-8609 nnns volume:98 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 48.68 Aquakultur VZ AR 98
language	English
source	Enthalten in Aquacultural engineering 98 volume:98
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topic_facet	Seedling production Egg quality Deep learning Convolutional neural network
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title	Normal hatching rate estimation for bulk samples of Pacific bluefin tuna (
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title_sort	normal hatching rate estimation for bulk samples of pacific bluefin tuna (
title_auth	Normal hatching rate estimation for bulk samples of Pacific bluefin tuna (
abstract	In the aquaculture of Pacific bluefin tuna (PBT, Thunnus orientalis), low survival rates during the larval stage remain a significant issue for the stable supply of seedlings reared from eggs. Egg quality is an important factor affecting seedling production success. If egg quality can be evaluated before hatching, the efficiency of seedling production will be improved by selectively cultivating the seedlings with a high survival rate. In our previous study, we developed a system using a convolutional neural network to evaluate PBT egg quality by estimating the normal hatching rate. The system used an image containing one PBT egg as the input. To further improve the efficiency of the egg quality estimation system, we updated the system that estimates the egg quality of a bulk sample (approximately 30 eggs). Our results indicated that the proposed system can estimate the normal hatching rate of the bulk sample with higher accuracy than the visual inspections of three field experts and visualize the normal hatching rate of each egg in the bulk sample. The proposed system will serve as a foundation for assessing the quality of PBT eggs and increasing the efficiency of seedling production.
abstractGer	In the aquaculture of Pacific bluefin tuna (PBT, Thunnus orientalis), low survival rates during the larval stage remain a significant issue for the stable supply of seedlings reared from eggs. Egg quality is an important factor affecting seedling production success. If egg quality can be evaluated before hatching, the efficiency of seedling production will be improved by selectively cultivating the seedlings with a high survival rate. In our previous study, we developed a system using a convolutional neural network to evaluate PBT egg quality by estimating the normal hatching rate. The system used an image containing one PBT egg as the input. To further improve the efficiency of the egg quality estimation system, we updated the system that estimates the egg quality of a bulk sample (approximately 30 eggs). Our results indicated that the proposed system can estimate the normal hatching rate of the bulk sample with higher accuracy than the visual inspections of three field experts and visualize the normal hatching rate of each egg in the bulk sample. The proposed system will serve as a foundation for assessing the quality of PBT eggs and increasing the efficiency of seedling production.
abstract_unstemmed	In the aquaculture of Pacific bluefin tuna (PBT, Thunnus orientalis), low survival rates during the larval stage remain a significant issue for the stable supply of seedlings reared from eggs. Egg quality is an important factor affecting seedling production success. If egg quality can be evaluated before hatching, the efficiency of seedling production will be improved by selectively cultivating the seedlings with a high survival rate. In our previous study, we developed a system using a convolutional neural network to evaluate PBT egg quality by estimating the normal hatching rate. The system used an image containing one PBT egg as the input. To further improve the efficiency of the egg quality estimation system, we updated the system that estimates the egg quality of a bulk sample (approximately 30 eggs). Our results indicated that the proposed system can estimate the normal hatching rate of the bulk sample with higher accuracy than the visual inspections of three field experts and visualize the normal hatching rate of each egg in the bulk sample. The proposed system will serve as a foundation for assessing the quality of PBT eggs and increasing the efficiency of seedling production.
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title_short	Normal hatching rate estimation for bulk samples of Pacific bluefin tuna (
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up_date	2024-07-06T19:09:58.326Z
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Egg quality is an important factor affecting seedling production success. If egg quality can be evaluated before hatching, the efficiency of seedling production will be improved by selectively cultivating the seedlings with a high survival rate. In our previous study, we developed a system using a convolutional neural network to evaluate PBT egg quality by estimating the normal hatching rate. The system used an image containing one PBT egg as the input. To further improve the efficiency of the egg quality estimation system, we updated the system that estimates the egg quality of a bulk sample (approximately 30 eggs). Our results indicated that the proposed system can estimate the normal hatching rate of the bulk sample with higher accuracy than the visual inspections of three field experts and visualize the normal hatching rate of each egg in the bulk sample. 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Normal hatching rate estimation for bulk samples of Pacific bluefin tuna (

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