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Shrimp Shape Analysis by a Chord Length Function Based Methodology

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Recent Trends in Image Processing and Pattern Recognition (RTIP2R 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1704))

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Abstract

The most expensive operational cost in shrimp farming and in every aquaculture system is feeding. To estimate the quantity of food is necessary to know the total biomass of the pond. Traditionally, this is done by taking samples and weighting, which is invasive and stress the animals. Non intrusive methods have been tried to estimate pond biomass using different technologies, being one of them computer vision. Computer vision faces several challenges, such as the problem of how to identify shrimps, count them, estimate their size and their mass. In this work, a chord length function based methodology is proposed as a viable alternative to analyze shrimp’s shape and count them, this methodology generates histograms of the shape of the shrimps and therefore, a set of statistical parameters (mean, median, mode, variance, standard deviation, maximun and minimum) to quantify shape and which can be useful to identify shrimps, estimate their sizes, and even find a relationship between morphometric measures with respect to biomass.

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Acknowledgements

We acknowledge the financial support provided by Conacyt to the first author, with the scholarship 709298, and by the TecNM with the project 15290.22-P

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Authors and Affiliations

  1. Tecnológico Nacional de México/I.T. de Hermosillo, División de Estudios de Posgrado e Investigación, Hermosillo, Sonora, Mexico

    Fernando J. Ramírez-Coronel, Oscar M. Rodríguez-Elías & Omar Hernández-González

  2. Tecnológico Nacional de México/I.T. de Tuxtla Gutiérrez, División de Estudios de Posgrado e Investigación, Tuxtla Gutiérrez, Chiapas, Mexico

    Madaín Pérez-Patricio

  3. Universidad Estatal de Sonora, Hermosillo, Sonora, Mexico

    Edgard Esquer-Miranda

  4. Universidad de Sonora, Departamento de Matemáticas, Hermosillo, Sonora, Mexico

    Julio Waissman-Vilanova

  5. Tecnológico Nacional de México/I.T. de Chihuahua, División de Estudios de Posgrado e Investigación, Chihuahua, Chihuahua, Mexico

    Mario I. Chacón-Murguía

Authors
  1. Fernando J. Ramírez-Coronel
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  2. Oscar M. Rodríguez-Elías
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  3. Madaín Pérez-Patricio
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  4. Edgard Esquer-Miranda
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  5. Julio Waissman-Vilanova
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  6. Mario I. Chacón-Murguía
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  7. Omar Hernández-González
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Corresponding author

Correspondence to Oscar M. Rodríguez-Elías .

Editor information

Editors and Affiliations

  1. University of South Dakota, Vermillion, SD, USA

    KC Santosh

  2. Texas A&M University, College Station, TX, USA

    Ayush Goyal

  3. University of Luxembourg, Luxembourg, Luxembourg

    Djamila Aouada

  4. University of Derby, Derby, UK

    Aaisha Makkar

  5. University of Minnesota, Minneapolis, MN, USA

    Yao-Yi Chiang

  6. IIIT Allahabad, Allahabad, India

    Satish K Singh

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Ramírez-Coronel, F.J. et al. (2023). Shrimp Shape Analysis by a Chord Length Function Based Methodology. In: Santosh, K., Goyal, A., Aouada, D., Makkar, A., Chiang, YY., Singh, S.K. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2022. Communications in Computer and Information Science, vol 1704. Springer, Cham. https://doi.org/10.1007/978-3-031-23599-3_15

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  • DOI: https://doi.org/10.1007/978-3-031-23599-3_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23598-6

  • Online ISBN: 978-3-031-23599-3

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