Deep learning for smart fish farming: applications, opportunities and challenges-Reference-Cited by-同舟云学术

Deep learning for smart fish farming: applications, opportunities and challenges

Published:2020-06-29 Issue:1 Volume:13 Page:66-90
ISSN:1753-5123
Container-title:Reviews in Aquaculture
language:en
Short-container-title:Rev. Aquacult.

Author:

Yang Xinting¹²³,Zhang Song¹²³⁴,Liu Jintao¹²³⁵,Gao Qinfeng⁶,Dong Shuanglin⁶,Zhou Chao¹²³^ORCID

Affiliation:

1. Beijing Research Center for Information Technology in Agriculture Beijing China

2. National Engineering Research Center for Information Technology in Agriculture Beijing China

3. National Engineering Laboratory for Agri‐product Quality Traceability Beijing China

4. Tianjin University of Science and Technology Tianjin China

5. Department of Computer Science University of Almeria Almeria Spain

6. Key Laboratory of Mariculture Ministry of Education Ocean University of China Qingdao Shandong Province China

Funder

National Key Technology R&D Program of China

Youth Research Fund of Beijing Academy of Agricultural and Forestry Sciences

Beijing Excellent Talents Development Project

Publisher

Wiley

Subject

Management, Monitoring, Policy and Law,Ecology,Aquatic Science

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/raq.12464

Reference144 articles.

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3. Fish species identification using a convolutional neural network trained on synthetic data

4. Image‐based, unsupervised estimation of fish size from commercial landings using deep learning;Álvarez‐Ellacuría A;ICES Journal of Marine Science,2019

5. BahrampourS RamakrishnanN SchottL ShahMJCS(2015)Comparative Study of Deep Learning Software Frameworks.arXiv preprint arXiv:1511.06435 2015.

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