01582naa a2200337 a 450000100080000000500110000800800410001902400270006010000180008724501340010526000090023952005910024865000160083965000280085565000250088365000130090865300160092165300220093765300290095965300230098870000170101170000190102870000200104770000220106770000210108970000160111070000250112670000200115170000210117177300520119221516932023-08-01       2023    bl uuuu     u00u1 u    #d7 a10.1111/age.133022DOI1 aARIEDE, R. B.  aComputer vision system using deep learning to predict rib and loin yield in the fish Colossoma macropomum.h[electronic resource]  c2023  aThe present study aimed to develop an automated CVS by deep learning that allows high-throughput phenotyping of biometric traits to predict loin and rib yield in tambaqui. The dataset used for this study also includes the annotated images of the CVS recently developed in P. mesopotamicus (Freitas et al., 2023), which is a closely related species of tambaqui, as few images were available for tambaqui. Moreover, genetic parameters of heritability and correlation were estimated for several biometric traits, as well as loin and rib yield, in a farmed population of tambaqui in Brazil.  aAquaculture  aArtificial intelligence  aColossoma Macropomum  aTambaqui  aAquacultura  aGenetic selection  aInteligência artificial  aSmart fish farming1 aLEMOS, C. G.1 aBATISTA, F. M.1 aOLIVEIRA, R. R.1 aAGUDELO, J. F. G.1 aBORGES, C. H. S.1 aIOPE, R. L.1 aO'SULLIVAN, F. L. A.1 aBREGA, J. R. F.1 aHASHIMOTO, D. T.  tAnimal Geneticsgv. 54, n. 3, p. 375-388, 2023.