Supervised computer vision system for weight group classification of fingerlings

  • Adair da S. O. Junior UFMS
  • Ana Karina V. da Silva UCDB
  • Marcio C. B. Pache IFMS
  • Diego A. Sant’Ana IFMS
  • Alexsandro M. Carneiro
  • Pedro L. F. Albuquerque UCDB
  • Vanir Garcia IFMS
  • Fabricio de L. Weber University of Nebraska–incoln
  • Vanessa A. Weber University of Nebraska–incoln
  • Hemerson Pistori UCDB


Classifying weight ranges is essential in fish farms since they are sold according to the fingerlings’ minimum size, uniformity, and maximum size. Therefore, evaluating the batches’ quality is fundamental, which analyzes the growth rate, feed conversion, and survival rate, among others. This paper aims to classify weight ranges for fingerlings of Pintado Real using supervised learning techniques. For this purpose, 60 images were captured, containing a 10-cent Real coin and a ruler next to each fingerling. The software was designed to extract attributes from the images and then serve as inputs for Weka’s training algorithms. The J48 algorithm obtained a performance 76.66% in the accuracy metric, and the ANOVA shows no statistical difference. This result is promising since the dataset of images taken from varying distances is a situation that is often common in this type of collection, and the proposed software takes care of standardizing the scale.

Palavras-chave: weight range classification, image processing, smart fish farm, Pseudoplatystoma corruscans


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O. JUNIOR, Adair da S. et al. Supervised computer vision system for weight group classification of fingerlings. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 18. , 2023, São Bernardo do Campo/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 142-147. DOI: