Guided classification measurement of body condition in beef cows

Authors

DOI:

https://doi.org/10.31285/AGRO.27.1165

Keywords:

body condition score, digital imaging, beef cattle, information technology

Abstract

The use of predictive techniques based on image acquisition at field level is presented to improve livestock production. With this, the farmer can plan the management of the herd and the need for supplemental feeding. The use of the Body Condition Score (BCS) measured by visual assessment has been proposed as a method of subjective evaluation of the nutritional status of cattle. Studies show that a good BCS at calving allows increases in the order of 10 to 15% in the following pregnancy rate of the herd. This increase has a significant impact on farm productivity. Although the benefits of the visual assessment scale are recognized, the percentage of breeders using this tool is still low, the main reason being the lack of trained raters to record the BCS. The objective of this study was to develop a practical, repetitive, and non-invasive method to evaluate BCS through a guided grading process using images taken in the field. The results show that the BCS determination method proposed in this paper is presented as a simple and economical tool to evaluate BCS, so that it can be accepted by the breeder for its simplicity and benefits. Additionally, it can serve as a tutorial for the acquisition of experience in calibrating BCS in breeding cows.

Downloads

Download data is not yet available.

References

Arias P, Pini A, Sanguinetti G, Sprechmann P. Segmentación con información a priori de forma aplicada a Sistema de Valoración Cárnica [grade’s thesis]. Montevideo (UY): Universidad de la República, Facultad de Ingeniería; 2005. 150p.

Armand-Ugón J, Invernizzi A, Secco A. Generación de una base de datos en el programa Cond_corp para la raza Braford [grade’s thesis on Internet]. Montevideo (UY): Universidad de la República, Facultad de Agronomía; 2016 [cited 2023 Jul 31]. 50p. Available from: https://hdl.handle.net/20.500.12008/19724

Arotxarena A, Irazábal P. Clasificación guiada de imágenes para la determinación de la condición corporal en ganado Hereford [grade’s thesis on Internet]. Montevideo (UY): Universidad de la República, Facultad de Agronomía; 2014 [cited 2023 Jul 31]. 47p. Available from: https://hdl.handle.net/20.500.12008/8762

Azambuja N, Carriquiry F, Pérez M, Sicardi I. Validación y clasificación guiada de imágenes para la determinación de la condición corporal en ganado Aberdeen Angus y cruza Angus-Hereford [grade’s thesis on Internet]. Montevideo (UY): Universidad de la República, Facultad de Agronomía; 2015 [cited 2023 Jul 31]. 39p. Available from: https://hdl.handle.net/20.500.12008/8722

Azzaro G, Caccamo M, Ferguson JD, Battiato S, Farinella GM, Guarnera GC, Puglisi G, Petrigiliero R, Licitra G. Objective estimation of body condition score by modeling. J Dairy Sci. 2011;94:2126-37. Doi: 10.3168/jds.2010-3467.

Azzaro G, Caccamo M, Licitra G, Ferguson JD. Estimation of cow's body condition score from images. In: International Workshop on Visual Observation and Analysis of Animal and Insect Behavior (VAIB) [Internet]. [place unknown]: ICPR; 2010 [cited 2023 Jul 31]. 4p. Available from: https://homepages.inf.ed.ac.uk/rbf/VAIB10PAPERS/gfVAIB2010Final.pdf

Bercovich A, Edan Y, Alchanatis V, Moallem U, Parmet Y, Honig H, Maltz E, Antler A, Halachmi I. Development of an automatic cow body condition scoring using body shape signature and Fourier descriptors. J Dairy Sci. 2013;96:8047-59. Doi: 10.3168/jds.2013-6568.

Bewley JM, Peacock AM, Lewis O, Boyce RE, Roberts DJ, Coffey MP, Kenyon SJ, Schutz MM. Potential for estimation of body condition scores in dairy cattle from digital images. J Dairy Sci. 2008;91(9):3439-53. Doi: 10.3168/jds.2007-0836.

Bianculli M, Duffour AY, Lezama J. Proyecto Ojo de Bife: Extracción automática de información de imágenes color del músculo longissimus dorsi [grade’s thesis on Internet]. Montevideo (UY): Universidad de la República, Facultad de Ingeniería; 2007 [cited 2023 Jul 31]. 145p. Available from: https://hdl.handle.net/20.500.12008/2849

Bomio S, Cabrera F, Horta J. Validación del programa cond_corp en el rodeo Hereford de la Estación Experimental Mario Alberto Cassinoni [grade’s thesis on Internet]. Montevideo (UY): Universidad de la República, Facultad de Agronomía; 2015 [cited 2023 Jul 31]. 51p. Available from: https://hdl.handle.net/20.500.12008/8718

Cancela P, Reyes F, Rodríguez P, Randall G, Fernández A. Automatic object detection using shape information in ultrasound images. In: Proceedings 2003 International Conference on Image Processing. Vol. 3. Tampere: IEEE; 2003. pp. 417-20. Doi: 10.1109/ICIP.2003.1247270.

Earle DF. A guide to scoring dairy cow condition. J Agric. 1976;74:228-31.

Ferguson JD, Azzaro G, Licitra G. Body condition using digital images. J Dairy Sci. 2006;89(10):3833-41. Doi: 10.3168/jds.S0022-0302(06)72425-0.

Gaimari K, Peñagaricano E. Entrenamiento en la calificación de la condición corporal mediante el software Cond_corp [grade’s thesis on Internet]. Montevideo (UY): Universidad de la República, Facultad de Agronomía; 2017 [cited 2023 Jul 31]. 50p. Available from: https://hdl.handle.net/20.500.12008/18647

Halachmi I, Klopcic M, Polak P, Roberts DJ, Bewley JM. Automatic assessment of dairy cattle body condition score using thermal imaging. Comput Electron Agric. 2013;99:35-40. Doi: 10.1016/j.compag.2013.08.012.

Halachmi I, Klopcic M, Polak P. Body condition scoring using thermal camera. In: Proceedings of the 20th Conference on Dairy Science. Jerusalem: ICBA; 2008. pp. 26.

Krukowski M. Automatic determination of body condition score of dairy cows from 3D images [master’s thesis]. Stockholm (SE): KTH Royal Institute of Technology; 2009. 89p.

Ministerio de Ganadería, Agricultura y Pesca, SNIG (UY). Conceptos sobre trazabilidad individual [Internet]. Montevideo: MGAP; 2022 [cited 2023 Jul 31]. Available from: https://www.snig.gub.uy/principal/snig-principal-trazabilidad-trazabilidad-individual-prueba

Oborsky M, Pachón F. Validación de la metodología del programa cond_corp para la calificación de la condición corporal en vacas de diferentes genotipos en situaciones reales de producción [grade’s thesis on Internet]. Montevideo (UY): Universidad de la República, Facultad de Agronomía; 2016 [cited 2023 Jul 31]. 38p. Available from: https://hdl.handle.net/20.500.12008/19706

Qiao Y, Kong H, Clark C, Lomax S, Su D, Eiffert S, Sukkarieh S. Intelligent perception-based cattle lameness detection and behaviour recognition: a review. Animals (Basel). 2021;11(11):3033. Doi: 10.3390/ani11113033.

Shelley AN, Lau DL, Stone AE, Bewley JM. Short communication: measuring feed volume and weight by machine vision. J Dairy Sci. 2016;99:386-91. Doi: 10.3168/jds.2014-8964.

Song X, Bokkers EAM, Van Mourik S, Groot Koerkamp PWG, Van der Tol PPJ. Automated body condition scoring of dairy cows using 3-dimensional feature extraction from multiple body regions. J Dairy Sci. 2019;102(5):4294-308. Doi: 10.3168/jds.2018-15238.

Spoliansky R, Edan Y, Parmet Y, Halachm I. Development of automatic body condition scoring using a low-cost 3-dimensional Kinect camera. J Dairy Sci. 2016;99:7714-23. Doi: 10.3168/jds.2015-10607.

Tedín R, Becerra JA, Duro RJ, Ismael Martínez I. Towards automatic estimation of the body condition score of dairy cattle using hand-held images and active shape models. In: Graña M, Toro C, Posada J, Howlett RJ, Jain LC, editors. Advances in knowledge-based and intelligent information and engineering systems. Amsterdam: IOS Press; 2012. pp. 2150-9. Doi: 10.3233/978-1-61499-105-2-2150.

Vizcarra JA, Ibañez W, Orcasberro R. Repeatability, and reproducibility of two scales for estimating body condition in Hereford cows. Investigaciones Agronómicas. 1986;(7):45-7.

Yukun S, Pengju H, Yujie W, Ziqi C, Yang L, Baisheng D, Runze L, Yonggen Z. Automatic monitoring system for individual dairy cows based on a deep learning framework that provides identification via body parts and estimation of body condition score. J Dairy Sci. 2019;102(11):10140­51. Doi: 10.3168/jds.2018-16164.

Zin TT, Seint PT, Tin P, Horii Y, Kobayashi I. Body condition score estimation based on regression analysis using a 3D, camera. Sensors (Basel). 2020;20(13):3705. Doi: 10.3390/s20133705.

Published

2023-08-28

How to Cite

1.
Espasandin AC, Larracharte Cardoso AG, Pérez López N. Guided classification measurement of body condition in beef cows. Agrocienc Urug [Internet]. 2023 Aug. 28 [cited 2024 May 4];27:e1165. Available from: https://agrocienciauruguay.uy/index.php/agrociencia/article/view/1165

Issue

Section

Animal production and pastures
QR Code

Altmetric

Article metrics
Abstract views
Galley vies
PDF Views
HTML views
Other views