Review and analysis of computational techniques and methods for body condition score estimation on cows

Authors

  • Juan Rodríguez Álvarez Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina
  • Mauricio Arroqui Agencia Nacional de Promoción Científica y Tecnológica, Argentina
  • Pablo Mangudo Agencia Nacional de Promoción Científica y Tecnológica, Argentina
  • Juan Toloza Agencia Nacional de Promoción Científica y Tecnológica, Argentina
  • Daniel Jatip Agencia Nacional de Promoción Científica y Tecnológica, Argentina
  • Juan M. Rodríguez Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina
  • Alejandro Zunino Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina
  • Cristian Mateos Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina
  • Claudio Machado Agencia Nacional de Promoción Científica y Tecnológica, Argentina

Keywords:

precision livestock, body condition score, machine learning, deep learning, image analysis, convolutional neural networks

Abstract

BCS (Body Condition Score) is a method used to estimate body fat reserves and accumulated energy balance of cows. BCS heavily influences milk production, reproduction, and health of cows. Therefore, it is important to monitor BCS to achieve better animal response, but this is a time-consuming and subjective task performed oftentimes visually by expert scorers. These problems are the motivation behind several studies, which have tried to automate BCS of dairy cows by applying image analysis and machine learning techniques. This work analyzes these studies pointing out their main advantages and drawbacks, which allow us in turn to identify new research and development opportunities to improve overall automatic BCS estimation.

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Published

2018-07-01

How to Cite

Rodríguez Álvarez, J., Arroqui, M., Mangudo, P., Toloza, J., Jatip, D., Rodríguez, J. M., Zunino, A., Mateos, C., & Machado, C. (2018). Review and analysis of computational techniques and methods for body condition score estimation on cows. SADIO Electronic Journal of Informatics and Operations Research, 17(2), 48-65. https://revistas.unlp.edu.ar/ejs/article/view/17600