Journal of Animal Breeding and Genomics (J Anim Breed Genom)
Thisarani Ediriweera1,*, Prabuddha Manjula2
1Department of Bio-AI Convergence, Chungnam National University, Daejeon 34134, South Korea
2Division of Animal and Dairy Science, Chungnam National University, Daejeon 34134, South Korea
Correspondence to Thisarani Ediriweera, E-mail: tkediriweera@gmail.com
Volume 5, Number 3, Pages 107-112, September 2021.
Journal of Animal Breeding and Genomics 2021, 5(3), 107-112. https://doi.org/10.12972/jabng.20210011
Received on 23 September, 2021, Revised on 29 September, 2021, Accepted on 30 September, 2021, Published on 30 September, 2021.
Copyright © 2021 Korean Society of Animal Breeding and Genetics.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0).
This concise account on aquaculture, aquaculture genetics, and emerging trends of its convergence with machine learning, a sub-class of artificial intelligence provides, succinct overviews for each of the disciplines separately, their basics, and machine learning approaches in aquaculture genetics, in a consolidative manner to brief their status, applications and prospects
Aquaculture, Convergence, Genetics, Machine learning
This work was also partly supported and partly supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2020-0-01441, Artificial Intelligence Convergence Research Center (Chungnam National University)) and Korea Evaluation Institute of Industrial Technology (KEIT) grant funded by the Korea government (MOTIE).