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- W4283452146 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." @default.
- W4283452146 created "2022-06-26" @default.
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- W4283452146 date "2022-08-01" @default.
- W4283452146 modified "2023-09-26" @default.
- W4283452146 title "Normal hatching rate estimation for bulk samples of Pacific bluefin tuna (Thunnus orientalis) eggs using deep learning" @default.
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- W4283452146 doi "https://doi.org/10.1016/j.aquaeng.2022.102274" @default.
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