Literature DB >> 27344390

Technical note: Validation of a commercial system for the continuous and automated monitoring of dairy cow activity.

E Tullo1, I Fontana2, D Gottardo1, K H Sloth3, M Guarino1.   

Abstract

Current farm sizes do not allow the precise identification and tracking of individual cows and their health and behavioral records. Currently, the application of information technology within intensive dairy farming takes a key role in proper routine management to improve animal welfare and to enhance the comfort of dairy cows. An existing application based on information technology is represented by the GEA CowView system (GEA Farm Technologies, Bönen, Germany). This system is able to detect and monitor animal behavioral activities based on positioning, through the creation of a virtual map of the barn that outlines all the areas where cows have access. The aim of this study was to validate the accuracy, sensitivity, and specificity of data provided by the CowView system. The validation was performed by comparing data automatically obtained from the CowView system with those obtained by a manual labeling procedure performed on video recordings. Data used for the comparisons were represented by the zone-related activities performed by the selected dairy cows and were classified into 2 categories: activity and localization. The duration in seconds of each of the activities/localizations detected both with the manual labeling and with the automated system were used to evaluate the correlation coefficients among data; and subsequently the accuracy, sensitivity, specificity, and positive and negative predictive values of the automated monitoring system were calculated. The results of this validation study showed that the CowView automated monitoring system is able to identify the cow localization/position (alley, trough, cubicles) with high reliability in relation to the zone-related activities performed by dairy cows (accuracy higher than 95%). The results obtained support the CowView system as an innovative potential solution for the easier management of dairy cows.
Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  automated monitoring system; dairy cow; sensor; validation

Mesh:

Year:  2016        PMID: 27344390     DOI: 10.3168/jds.2016-11014

Source DB:  PubMed          Journal:  J Dairy Sci        ISSN: 0022-0302            Impact factor:   4.034


  6 in total

1.  Space-use patterns highlight behavioural differences linked to lameness, parity, and days in milk in barn-housed dairy cows.

Authors:  Jorge A Vázquez Diosdado; Zoe E Barker; Holly R Hodges; Jonathan R Amory; Darren P Croft; Nick J Bell; Edward A Codling
Journal:  PLoS One       Date:  2018-12-19       Impact factor: 3.240

2.  Now You See Me: Convolutional Neural Network Based Tracker for Dairy Cows.

Authors:  Oleksiy Guzhva; Håkan Ardö; Mikael Nilsson; Anders Herlin; Linda Tufvesson
Journal:  Front Robot AI       Date:  2018-09-19

3.  Proximity Interactions in a Permanently Housed Dairy Herd: Network Structure, Consistency, and Individual Differences.

Authors:  Kareemah Chopra; Holly R Hodges; Zoe E Barker; Jorge A Vázquez Diosdado; Jonathan R Amory; Tom C Cameron; Darren P Croft; Nick J Bell; Edward A Codling
Journal:  Front Vet Sci       Date:  2020-12-07

Review 4.  Opportunities to Harness High-Throughput and Novel Sensing Phenotypes to Improve Feed Efficiency in Dairy Cattle.

Authors:  Cori J Siberski-Cooper; James E Koltes
Journal:  Animals (Basel)       Date:  2021-12-22       Impact factor: 2.752

5.  Effects of Climatic Conditions on the Lying Behavior of a Group of Primiparous Dairy Cows.

Authors:  Emanuela Tullo; Gabriele Mattachini; Elisabetta Riva; Alberto Finzi; Giorgio Provolo; Marcella Guarino
Journal:  Animals (Basel)       Date:  2019-10-26       Impact factor: 2.752

6.  Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review.

Authors:  Kaitlin Wurtz; Irene Camerlink; Richard B D'Eath; Alberto Peña Fernández; Tomas Norton; Juan Steibel; Janice Siegford
Journal:  PLoS One       Date:  2019-12-23       Impact factor: 3.240

  6 in total

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