02016naa a2200265 a 450000100080000000500110000800800410001902400480006010000220010824501440013026000090027452012020028365000280148565000200151365000190153365000090155265000100156165300250157165300180159665300210161465300290163565300210166465300250168577300400171021484252022-11-18       2022    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.3390/ fishes70603352DOI1 aBARBEDO, J. G. A.  aA review on the use of computer vision and artificial intelligence for fish recognition, monitoring, and management.h[electronic resource]  c2022  aAbstract: Computer vision has been applied to fish recognition for at least three decades. With the inception of deep learning techniques in the early 2010s, the use of digital images grew strongly, and this trend is likely to continue. As the number of articles published grows, it becomes harder to keep track of the current state of the art and to determine the best course of action for new studies. In this context, this article characterizes the current state of the art by identifying the main studies on the subject and briefly describing their approach. In contrast with most previous reviews related to technology applied to fish recognition, monitoring, and management, rather than providing a detailed overview of the techniques being proposed, this work focuses heavily on the main challenges and research gaps that still remain. Emphasis is given to prevalent weaknesses that prevent more widespread use of this type of technology in practical operations under real-world conditions. Some possible solutions and potential directions for future research are suggested, as an effort to bring the techniques developed in the academy closer to meeting the requirements found in practice.  aArtificial intelligence  aComputer vision  aDigital images  aFish  aPeixe  aAprendizado profundo  aDeep learning  aImagens digitais  aInteligência artificial  aMachine learning  aVisão computacional  tFishesgv. 7, n. 6, 335, Dec. 2022.