Counting, locating, and sizing of shrimp larvae based on density map regression-Reference-Cited by-同舟云学术

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Counting, locating, and sizing of shrimp larvae based on density map regression

Published:2023-11-02 Issue: Volume: Page:
ISSN:0967-6120
Container-title:Aquaculture International
language:en
Short-container-title:Aquacult Int

Author:

Zhou Chao,Yang Guowei,Sun Lihui,Wang Shifa,Song Weipeng,Guo Jianlin

Funder

"Three Rural and Nine Party" Science and Technology Cooperation Plan of Zhejiang Province

Science Technology Department of Huzhou City

Publisher

Springer Science and Business Media LLC

Subject

Agronomy and Crop Science,Aquatic Science

Link

https://link.springer.com/content/pdf/10.1007/s10499-023-01316-z.pdf

Reference41 articles.

1. AlFaris NA, Alshammari GM, AlTamimi JZ, AlMousa LA, Alagal RI, Alkehayez NM, Aljabryn DH, Alsayadi MM, Yahya MA (2022) Evaluating the effects of different processing methods on the nutritional composition of shrimp and the antioxidant activity of shrimp powder. Saudi J Biol Sci 29(1):640–649. https://doi.org/10.1016/j.sjbs.2021.09.029

2. Boles WW, Geva S, Busch A (1999) An image processing approach for estimating the number of live prawn larvae in water. In: ISSPA'99. Proceedings of the Fifth International Symposium on Signal Processing and its Applications, pp 571–574. https://doi.org/10.1109/ISSPA.1999.815737

3. Cao X, Wang Z, Zhao Y, Su F (2018) Scale aggregation network for accurate and efficient crowd counting. In: Proceedings of the European Conference on Computer Vision, pp 757–773. https://doi.org/10.1007/978-3-030-01228-1_45

4. Chen Y, Liu H, Yang L, Yu H, Li D, Mei S, Liu Y (2023) A lightweight detection method for the spatial distribution of underwater fish school quantification in intensive aquaculture. Aquacult Int 31(1):31–52. https://doi.org/10.1007/s10499-022-00963-y

5. Costa CS, Tetila EC, Astolf G, Sant’Ana DA, Brito Pache MC, Gonçalves AB, Garcia Zanoni VA, PicoliNucci HH, Diemer O, Pistori H (2019) A computer vision system for counting oocytes using images captured by smartphone. Aquacult Eng 87:102017. https://doi.org/10.1016/j.aquaeng.2019.102017

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