Beef Carcass Grading using Deep Convolutional Networks

  • Geazy Vilharva Menezes UFMS
  • Everton Castelão Tetila UCDB / UFGD
  • Diogo Nunes Gonçalves UFMS
  • Vanessa Aparecida de Moraes Weber UEMS / KeroW Soluções de precisão
  • Gabriel Toshio Hirokawa Higa UCDB
  • Marcelo Fontes Pereira UCDB
  • Marina de Nadai Bonin Gomes UFMS
  • Rodrigo da Costa Gomes EMBRAPA
  • Hemerson Pistori UFMS / UCDB


Beef carcass grading is an invaluable tool to ensure meat quality. In most of the Brazilian abattoirs, carcasses are graded through visual analysis by trained graders. In order to automate this process, we evaluate seven image deep learning models. For this purpose, a new dataset was created containing images of 670 bovine half-carcasses taken during regular operation in an abattoir. The images were graded by three professionals. All three experts agreed in only 9.9% of the cases, and two out of three graders agreed in 58.82%. The graders disagreed on 31.28% of the images. These results indicate the complexity of the problem. Nonetheless, an overall accuracy of 53% was achieved using convolutional neural networks, which is close to human performance, when the agreement between the graders is considered. Furthermore, an accuracy of around 91% can be achieved if the cases of disagreement are disregarded.


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MENEZES, Geazy Vilharva et al. Beef Carcass Grading using Deep Convolutional Networks. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 18. , 2023, São Bernardo do Campo/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 30-35. DOI: