Shrimp Shape Analysis by a Chord Length Function Based Methodology

Fernando J. Ramírez-Coronel; Oscar M. Rodríguez-Elías; Madaín Pérez-Patricio; Edgard Esquer-Miranda; Julio Waissman-Vilanova; Mario I. Chacón-Murguía; Omar Hernández-González

doi:10.1007/978-3-031-23599-3_15

Shrimp Shape Analysis by a Chord Length Function Based Methodology

Fernando J. Ramírez-Coronel, Oscar M. Rodríguez-Elías^*, Madaín Pérez-Patricio, Edgard Esquer-Miranda, Julio Waissman-Vilanova, Mario I. Chacón-Murguía, Omar Hernández-González

^*Corresponding author for this work

Departamento de Matemáticas

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

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.

Original language	English
Title of host publication	Recent Trends in Image Processing and Pattern Recognition - 5th International Conference, RTIP2R 2022, Revised Selected Papers
Editors	KC Santosh, Ayush Goyal, Djamila Aouada, Aaisha Makkar, Yao-Yi Chiang, Satish K Singh
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	205-219
Number of pages	15
ISBN (Print)	9783031235986
DOIs	https://doi.org/10.1007/978-3-031-23599-3_15
State	Published - 2023
Event	5th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2022 - Kingsville, United States Duration: 1 Dec 2022 → 2 Dec 2022

Publication series

Name	Communications in Computer and Information Science
Volume	1704 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	5th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2022
Country/Territory	United States
City	Kingsville
Period	1/12/22 → 2/12/22

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Chord length function
Shape analysis
Shrimp biomass estimation
Shrimp count

Access to Document

10.1007/978-3-031-23599-3_15

Cite this

Ramírez-Coronel, F. J., Rodríguez-Elías, O. M., Pérez-Patricio, M., Esquer-Miranda, E., Waissman-Vilanova, J., Chacón-Murguía, M. I., & Hernández-González, O. (2023). Shrimp Shape Analysis by a Chord Length Function Based Methodology. In KC. Santosh, A. Goyal, D. Aouada, A. Makkar, Y.-Y. Chiang, & S. K. Singh (Eds.), Recent Trends in Image Processing and Pattern Recognition - 5th International Conference, RTIP2R 2022, Revised Selected Papers (pp. 205-219). (Communications in Computer and Information Science; Vol. 1704 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-23599-3_15

Ramírez-Coronel, Fernando J. ; Rodríguez-Elías, Oscar M. ; Pérez-Patricio, Madaín et al. / Shrimp Shape Analysis by a Chord Length Function Based Methodology. Recent Trends in Image Processing and Pattern Recognition - 5th International Conference, RTIP2R 2022, Revised Selected Papers. editor / KC Santosh ; Ayush Goyal ; Djamila Aouada ; Aaisha Makkar ; Yao-Yi Chiang ; Satish K Singh. Springer Science and Business Media Deutschland GmbH, 2023. pp. 205-219 (Communications in Computer and Information Science).

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title = "Shrimp Shape Analysis by a Chord Length Function Based Methodology",

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{\textquoteright}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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Ramírez-Coronel, FJ, Rodríguez-Elías, OM, Pérez-Patricio, M, Esquer-Miranda, E, Waissman-Vilanova, J, Chacón-Murguía, MI & Hernández-González, O 2023, Shrimp Shape Analysis by a Chord Length Function Based Methodology. in KC Santosh, A Goyal, D Aouada, A Makkar, Y-Y Chiang & SK Singh (eds), Recent Trends in Image Processing and Pattern Recognition - 5th International Conference, RTIP2R 2022, Revised Selected Papers. Communications in Computer and Information Science, vol. 1704 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 205-219, 5th International Conference on Recent Trends in Image Processing and Pattern Recognition, RTIP2R 2022, Kingsville, United States, 1/12/22. https://doi.org/10.1007/978-3-031-23599-3_15

Shrimp Shape Analysis by a Chord Length Function Based Methodology. / Ramírez-Coronel, Fernando J.; Rodríguez-Elías, Oscar M.; Pérez-Patricio, Madaín et al.
Recent Trends in Image Processing and Pattern Recognition - 5th International Conference, RTIP2R 2022, Revised Selected Papers. ed. / KC Santosh; Ayush Goyal; Djamila Aouada; Aaisha Makkar; Yao-Yi Chiang; Satish K Singh. Springer Science and Business Media Deutschland GmbH, 2023. p. 205-219 (Communications in Computer and Information Science; Vol. 1704 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Rodríguez-Elías, Oscar M.

AU - Pérez-Patricio, Madaín

AU - Esquer-Miranda, Edgard

AU - Waissman-Vilanova, Julio

AU - Chacón-Murguía, Mario I.

AU - Hernández-González, Omar

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AB - 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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Ramírez-Coronel FJ, Rodríguez-Elías OM, Pérez-Patricio M, Esquer-Miranda E, Waissman-Vilanova J, Chacón-Murguía MI et al. Shrimp Shape Analysis by a Chord Length Function Based Methodology. In Santosh KC, Goyal A, Aouada D, Makkar A, Chiang YY, Singh SK, editors, Recent Trends in Image Processing and Pattern Recognition - 5th International Conference, RTIP2R 2022, Revised Selected Papers. Springer Science and Business Media Deutschland GmbH. 2023. p. 205-219. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-23599-3_15