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Brito Pache MC, Sant’Ana DA, Araújo Rozales JV, de Moraes Weber VA, Silva Oliveira Junior AD, Garcia V, Pistori H, Naka MH. Prediction of fingerling biomass with deep learning. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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Zenger KR, Khatkar MS, Jones DB, Khalilisamani N, Jerry DR, Raadsma HW. Genomic Selection in Aquaculture: Application, Limitations and Opportunities With Special Reference to Marine Shrimp and Pearl Oysters. Front Genet 2019; 9:693. [PMID: 30728827 PMCID: PMC6351666 DOI: 10.3389/fgene.2018.00693] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Accepted: 12/11/2018] [Indexed: 11/20/2022] Open
Abstract
Within aquaculture industries, selection based on genomic information (genomic selection) has the profound potential to change genetic improvement programs and production systems. Genomic selection exploits the use of realized genomic relationships among individuals and information from genome-wide markers in close linkage disequilibrium with genes of biological and economic importance. We discuss the technical advances, practical requirements, and commercial applications that have made genomic selection feasible in a range of aquaculture industries, with a particular focus on molluscs (pearl oysters, Pinctada maxima) and marine shrimp (Litopenaeus vannamei and Penaeus monodon). The use of low-cost genome sequencing has enabled cost-effective genotyping on a large scale and is of particular value for species without a reference genome or access to commercial genotyping arrays. We highlight the pitfalls and offer the solutions to the genotyping by sequencing approach and the building of appropriate genetic resources to undertake genomic selection from first-hand experience. We describe the potential to capture large-scale commercial phenotypes based on image analysis and artificial intelligence through machine learning, as inputs for calculation of genomic breeding values. The application of genomic selection over traditional aquatic breeding programs offers significant advantages through being able to accurately predict complex polygenic traits including disease resistance; increasing rates of genetic gain; minimizing inbreeding; and negating potential limiting effects of genotype by environment interactions. Further practical advantages of genomic selection through the use of large-scale communal mating and rearing systems are highlighted, as well as presenting rate-limiting steps that impact on attaining maximum benefits from adopting genomic selection. Genomic selection is now at the tipping point where commercial applications can be readily adopted and offer significant short- and long-term solutions to sustainable and profitable aquaculture industries.
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Affiliation(s)
- Kyall R Zenger
- Centre for Sustainable Tropical Fisheries and Aquaculture, College of Science and Engineering, James Cook University, Townsville, QLD, Australia.,ARC Research Hub for Advanced Prawn Breeding, James Cook University, Townsville, QLD, Australia
| | - Mehar S Khatkar
- ARC Research Hub for Advanced Prawn Breeding, James Cook University, Townsville, QLD, Australia.,Sydney School of Veterinary Science, Faculty of Science, The University of Sydney, Camden, NSW, Australia
| | - David B Jones
- Centre for Sustainable Tropical Fisheries and Aquaculture, College of Science and Engineering, James Cook University, Townsville, QLD, Australia
| | - Nima Khalilisamani
- ARC Research Hub for Advanced Prawn Breeding, James Cook University, Townsville, QLD, Australia.,Sydney School of Veterinary Science, Faculty of Science, The University of Sydney, Camden, NSW, Australia
| | - Dean R Jerry
- Centre for Sustainable Tropical Fisheries and Aquaculture, College of Science and Engineering, James Cook University, Townsville, QLD, Australia.,ARC Research Hub for Advanced Prawn Breeding, James Cook University, Townsville, QLD, Australia.,Tropical Futures Institute, James Cook University Singapore, Singapore, Singapore
| | - Herman W Raadsma
- ARC Research Hub for Advanced Prawn Breeding, James Cook University, Townsville, QLD, Australia.,Sydney School of Veterinary Science, Faculty of Science, The University of Sydney, Camden, NSW, Australia
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Lee D, Lee K, Kim S, Yang Y. Design of an optimum computer vision-based automatic abalone (Haliotis discus hannai) grading algorithm. J Food Sci 2015; 80:E729-33. [PMID: 25874500 DOI: 10.1111/1750-3841.12799] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2014] [Accepted: 12/04/2014] [Indexed: 11/30/2022]
Abstract
An automatic abalone grading algorithm that estimates abalone weights on the basis of computer vision using 2D images is developed and tested. The algorithm overcomes the problems experienced by conventional abalone grading methods that utilize manual sorting and mechanical automatic grading. To design an optimal algorithm, a regression formula and R(2) value were investigated by performing a regression analysis for each of total length, body width, thickness, view area, and actual volume against abalone weights. The R(2) value between the actual volume and abalone weight was 0.999, showing a relatively high correlation. As a result, to easily estimate the actual volumes of abalones based on computer vision, the volumes were calculated under the assumption that abalone shapes are half-oblate ellipsoids, and a regression formula was derived to estimate the volumes of abalones through linear regression analysis between the calculated and actual volumes. The final automatic abalone grading algorithm is designed using the abalone volume estimation regression formula derived from test results, and the actual volumes and abalone weights regression formula. In the range of abalones weighting from 16.51 to 128.01 g, the results of evaluation of the performance of algorithm via cross-validation indicate root mean square and worst-case prediction errors of are 2.8 and ±8 g, respectively.
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Affiliation(s)
- Donggil Lee
- Fisheries System Engineering Div, Natl. Fisheries Research & Development Inst, Korea
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He HJ, Wu D, Sun DW. Nondestructive Spectroscopic and Imaging Techniques for Quality Evaluation and Assessment of Fish and Fish Products. Crit Rev Food Sci Nutr 2014; 55:864-86. [DOI: 10.1080/10408398.2012.746638] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Dowlati M, de la Guardia M, Dowlati M, Mohtasebi SS. Application of machine-vision techniques to fish-quality assessment. Trends Analyt Chem 2012. [DOI: 10.1016/j.trac.2012.07.011] [Citation(s) in RCA: 70] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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