Inteligência artificial ajuda criadores brasileiros a selecionar características desejadas de peixes nativos
Se você olhar uma rede cheia de pacus (Piaractus mesopotamicus), não conseguirá distinguir a olho nu quais indivíduos terão descendentes com maior rendimento de filé ou ganho de peso mais rápido.
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)
Software desenvolvido por brasileiros mede pacus automaticamente usando inteligência artificial. Amostras com corpo redondo (topo) e corpo elíptico. À direita, peças selecionadas pelo programa via aprendizado de máquina. Crédito: Diogo Hashimoto/UNESP
Se você olhar uma rede cheia de pacus (Piaractus mesopotamicus), não conseguirá distinguir a olho nu quais indivíduos terão descendentes com maior rendimento de filé ou ganho de peso mais rápido.
Seu crescimento pode ser monitorado com uma fita métrica e podem ser pesados ??em uma balança. Os dados podem ser tabulados e comparados. Mas para criar uma população inteira com as características certas, cerca de 2.000 peixes por geração devem ser medidos e pesados, e a tarefa pode levar dias.
No Brasil, pesquisadores da Universidade Estadual Paulista (Unesp) resolveram esse problema desenvolvendo um software que usa inteligência artificial para fazer medições precisas em tempo real. Os resultados são publicados na revista Aquaculture .
O grupo de pesquisa trabalha há algum tempo no melhoramento genético dessa espécie nativa para aumentar a produtividade e diminuir os custos de produção.
"Quando você mede o peixe manualmente, você obtém menos dados, porque você o estressa e pode transmitir doenças que levam a surtos, sem contar o valioso tempo gasto. Automatizamos o processo, treinando a máquina com fotos de pacus e etiquetando cabeça, corpo , cintura pélvica e nadadeiras. Agora temos um aparelho portátil que pode ser levado a campo para fazer isso rapidamente e classificar os melhores animais", disse Diogo Hashimoto, último autor do artigo e professor do Centro de Aquicultura da Unesp em Jaboticabal.
Os pesquisadores usaram o aprendizado profundo , um dos mais recentes tipos de aprendizado de máquina, que produz resultados muito mais rapidamente, entre outras vantagens. O uso da inovação foi comandado por José Remo Ferreira Brega, professor do Departamento de Computação da Faculdade de Ciências de Bauru da Unesp e penúltimo autor do artigo.
No estudo mais recente, os pesquisadores se propuseram a distinguir o pacus de corpo redondo do oval. A espécie tem um corpo redondo na natureza, e acredita-se que essa característica influencie as decisões de compra dos consumidores. Os piscicultores o obtêm selecionando indivíduos com a relação altura x largura ideal para obter maiores rendimentos em lombo e costela, cortes preferidos pelos consumidores de peixes nativos como pacu e tambaqui.
Outras medidas, como tamanho da pelve ou relação cabeça-corpo, podem ser utilizadas como indicadores de rendimento de filé, taxa de crescimento e ganho de peso, por exemplo.
Melhoramento genético
Os criadores utilizam a seleção de fenótipos para melhoramento genético em todo o setor agropecuário brasileiro, que é líder mundial na produção de proteína animal a partir de frangos, bovinos de corte e suínos. Na piscicultura , porém, esse tipo de tecnologia está disponível apenas para salmão e tilápia, espécies exóticas e produzidas em massa no mundo todo, com a maioria das inovações vindas do exterior.
Embora a cadeia produtiva da tilápia no Brasil inclua pesquisa e desenvolvimento, o melhoramento das espécies nativas é incipiente. O software criado pelos pesquisadores para o pacu, porém, se mostrou mais resistente do que a tecnologia de seleção de fenótipos disponível para outras espécies, como a tilápia.
“Nosso programa consegue reconhecer e medir as diferentes partes do pacu mesmo na lateral do tanque, com poluição visual do fundo e condições de luz variáveis. Os sistemas desenvolvidos para a tilápia usam luz controlada e fundo padronizado”, disse Hashimoto.
A sistematização dos fenótipos do pacu em grandes bancos de dados permitirá aos criadores selecionar animais com maior precisão, percebendo o potencial de melhoria possibilitado por outro estudo realizado pelo grupo de Jaboticabal e publicado em 2021 . Neste artigo, eles descrevem polimorfismos de nucleotídeos simples (SNPs) para pacu e tambaqui (Colossoma macropomum). Essas mutações do código genético podem ser utilizadas no mapeamento genômico das características consideradas desejáveis, acelerando a seleção e o melhoramento.
O método convencional utilizado para medir o rendimento de filé ou lombo, por exemplo, consiste na eutanásia do animal e na pesagem de suas partes. Como resultado, o indivíduo se perde, deixando apenas seus irmãos, que são geneticamente semelhantes, mas não necessariamente possuem as características necessárias .
“A vantagem de integrar nosso software com dados genômicos é que podemos coletar as informações necessárias e manter o animal de interesse vivo para uso como reprodutor durante o processo de seleção”, disse Hashimoto.
Mais informações: Milena V. Freitas et al, Fenotipagem de alto rendimento por aprendizado profundo para incluir a forma corporal no programa de reprodução do pacu (Piaractus mesopotamicus), Aquicultura (2022). DOI: 10.1016/j.aquaculture.2022.738847