Review and analysis of computational techniques and methods for body condition score estimation on cows
Keywords:
precision livestock, body condition score, machine learning, deep learning, image analysis, convolutional neural networksAbstract
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.
Downloads
Published
Issue
Section
License
Copyright (c) 2018 Juan Rodríguez Álvarez, Mauricio Arroqui, Pablo Mangudo, Juan Toloza, Daniel Jatip, Juan M. Rodríguez, Alejandro Zunino, Cristian Mateos, Claudio Machado

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Those authors who have publications with this journal, agree with the following terms:
a. Authors will retain its copyright and will ensure the rights of first publication of its work to the journal, which will be at the same time subject to the Creative Commons Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0) allowing third parties to share the work as long as the author and the first publication on this journal is indicated.
b. Authors may elect other non-exclusive license agreements of the distribution of the published work (for example: locate it on an institutional telematics file or publish it on an monographic volume) as long as the first publication on this journal is indicated,
c. Authors are allowed and suggested to disseminate its work through the internet (for example: in institutional telematics files or in their website) before and during the submission process, which could produce interesting exchanges and increase the references of the published work. (see The effect of open Access)















