If you look at a net full of pacus (Piaractus mesopotamicus), you cannot distinguish with the naked eye which individuals will have descendants with higher fillet yield or faster weight gain, for example.
Their growth can be monitored with a tape measure, and they can be weighed on a scale. The data can be tabulated and compared. But to breed an entire population with the right characteristics, some 2,000 fish per generation must be measured and weighed, and the task can take days.
In Brazil, researchers at São Paulo State University (UNESP) have solved this problem by developing software that uses artificial intelligence to make accurate measurements in real time. The results are published in the journal Aquaculture.
The research group has been working for some time on genetic improvement of this native species to raise yields while lowering production costs (read more at: agencia.fapesp.br/36492).
“When you measure the fish manually, you get less data because you stress them and can transmit disease leading to outbreaks, not to mention the valuable time taken. We automated the process, training the machine with photos of pacus, and labeling head, body, pelvic girdle and fins. We now have a portable device that can be taken into the field to do this quickly and classify the best animals,” said Diogo Hashimoto, last author of the article. A professor at UNESP’s Aquaculture Center in Jaboticabal, Hashimoto heads a project supported by FAPESP.
The researchers used deep learning, one of the most recent kinds of machine learning, which produces results much faster, among other advantages. Use of the innovation was made possible by a project partially funded by Huawei do Brasil Telecomunicações and led by Jose Remo Ferreira Brega, a professor in the Department of Computing at UNESP’s School of Sciences in Bauru and penultimate author of the article.
In the latest study, the researchers set out to distinguish round-bodied from oval-bodied pacus. The species has a round body in the wild, and this trait is thought to influence buying decisions by consumers. Fish farmers obtain it by selecting individuals with the ideal height-to-width ratio to obtain higher yields in loin and rib, the cuts preferred by consumers of native fish such as pacu and tambaqui (read more at: agencia.fapesp.br/38000).
Other measurements, such as pelvis size or head-to-body ratio, can be used as indicators of fillet yield, growth rate and weight gain, for example.
Breeders use phenotype selection for genetic improvement throughout the Brazilian farm sector, which is the world leader in production of animal protein from chickens, beef cattle and pigs. In fish farming, however, this type of technology is available only for salmon and tilapia, both of which are exotic species and mass-produced worldwide, with most innovations coming from abroad.
Although Brazil’s tilapia production chain includes research and development, native species improvement is incipient. The software created by the researchers for pacu, however, proved more resilient than the phenotype selection technology available for other species, such as tilapia.
“Our program can recognize and measure the different parts of the pacu even at the side of the tank, with bottom visual pollution and variable light conditions. The systems developed for tilapia use controlled light and a standardized bottom,” Hashimoto said.
Systematization of pacu phenotypes in large databases will enable breeders to select animals with greater precision, realizing the potential for improvement made possible by another study conducted by the Jaboticabal group and published in 2021. In this article, they describe simple nucleotide polymorphisms (SNPs) for pacu and tambaqui (Colossoma macropomum). These genetic code mutations can be used in genomic mapping of the traits considered desirable, accelerating selection and improvement.
The conventional method used to measure fillet or loin yield, for example, entails euthanizing the animal and weighing its parts. The individual is lost as a result, leaving only its siblings, which are genetically similar but do not necessarily have the required traits.
“The advantage of integrating our software with genomic data is that we can collect the necessary information and keep the animal of interest alive for use as a reproducer during the selection process,” Hashimoto said.
About São Paulo Research Foundation (FAPESP)
The São Paulo Research Foundation (FAPESP) is a public institution with the mission of supporting scientific research in all fields of knowledge by awarding scholarships, fellowships and grants to investigators linked with higher education and research institutions in the State of São Paulo, Brazil. FAPESP is aware that the very best research can only be done by working with the best researchers internationally. Therefore, it has established partnerships with funding agencies, higher education, private companies, and research organizations in other countries known for the quality of their research and has been encouraging scientists funded by its grants to further develop their international collaboration. You can learn more about FAPESP at www.fapesp.br/en and visit FAPESP news agency at www.agencia.fapesp.br/en to keep updated with the latest scientific breakthroughs FAPESP helps achieve through its many programs, awards and research centers. You may also subscribe to FAPESP news agency at http://agencia.fapesp.br/subscribe
Subject of Research
High-throughput phenotyping by deep learning to include body shape in the breeding program of pacu (Piaractus mesopotamicus)
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