Continuous real-time cow identification by reading ear tags from live-stream video

In precision dairy farming there is a need for continuous and real-time availability of data on cows and systems. Data collection using sensors is becoming more common and it can be difficult to connect sensor measurements to the identification of the individual cow that was measured. Cows can be id...

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Main Authors: John W.M. Bastiaansen, Ina Hulsegge, Dirkjan Schokker, Esther D. Ellen, Bert Klandermans, Marjaneh Taghavi, Claudia Kamphuis
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-12-01
Series:Frontiers in Animal Science
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fanim.2022.846893/full
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author John W.M. Bastiaansen
Ina Hulsegge
Dirkjan Schokker
Esther D. Ellen
Bert Klandermans
Marjaneh Taghavi
Claudia Kamphuis
author_facet John W.M. Bastiaansen
Ina Hulsegge
Dirkjan Schokker
Esther D. Ellen
Bert Klandermans
Marjaneh Taghavi
Claudia Kamphuis
author_sort John W.M. Bastiaansen
collection DOAJ
description In precision dairy farming there is a need for continuous and real-time availability of data on cows and systems. Data collection using sensors is becoming more common and it can be difficult to connect sensor measurements to the identification of the individual cow that was measured. Cows can be identified by RFID tags, but ear tags with identification numbers are more widely used. Here we describe a system that makes the ear tag identification of the cow continuously available from a live-stream video so that this information can be added to other data streams that are collected in real-time. An ear tag reading model was implemented by retraining and existing model, and tested for accuracy of reading the digits on cows ear tag images obtained from two dairy farms. The ear tag reading model was then combined with a video set up in a milking robot on a dairy farm, where the identification by the milking robot was considered ground-truth. The system is reporting ear tag numbers obtained from live-stream video in real-time. Retraining a model using a small set of 750 images of ear tags increased the digit level accuracy to 87% in the test set. This compares to 80% accuracy obtained with the starting model trained on images of house numbers only. The ear tag numbers reported by real-time analysis of live-stream video identified the right cow 93% of the time. Precision and sensitivity were lower, with 65% and 41%, respectively, meaning that 41% of all cow visits to the milking robot were detected with the correct cow’s ear tag number. Further improvement in sensitivity needs to be investigated but when ear tag numbers are reported they are correct 93% of the time which is a promising starting point for future system improvements.
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spelling doaj.art-b0e6b1b9a6a74b2590226f54ec9c43712022-12-22T04:41:35ZengFrontiers Media S.A.Frontiers in Animal Science2673-62252022-12-01310.3389/fanim.2022.846893846893Continuous real-time cow identification by reading ear tags from live-stream videoJohn W.M. BastiaansenIna HulseggeDirkjan SchokkerEsther D. EllenBert KlandermansMarjaneh TaghaviClaudia KamphuisIn precision dairy farming there is a need for continuous and real-time availability of data on cows and systems. Data collection using sensors is becoming more common and it can be difficult to connect sensor measurements to the identification of the individual cow that was measured. Cows can be identified by RFID tags, but ear tags with identification numbers are more widely used. Here we describe a system that makes the ear tag identification of the cow continuously available from a live-stream video so that this information can be added to other data streams that are collected in real-time. An ear tag reading model was implemented by retraining and existing model, and tested for accuracy of reading the digits on cows ear tag images obtained from two dairy farms. The ear tag reading model was then combined with a video set up in a milking robot on a dairy farm, where the identification by the milking robot was considered ground-truth. The system is reporting ear tag numbers obtained from live-stream video in real-time. Retraining a model using a small set of 750 images of ear tags increased the digit level accuracy to 87% in the test set. This compares to 80% accuracy obtained with the starting model trained on images of house numbers only. The ear tag numbers reported by real-time analysis of live-stream video identified the right cow 93% of the time. Precision and sensitivity were lower, with 65% and 41%, respectively, meaning that 41% of all cow visits to the milking robot were detected with the correct cow’s ear tag number. Further improvement in sensitivity needs to be investigated but when ear tag numbers are reported they are correct 93% of the time which is a promising starting point for future system improvements.https://www.frontiersin.org/articles/10.3389/fanim.2022.846893/fulldeep learningimage analysisprecision farminganimal identificationnumber recognition
spellingShingle John W.M. Bastiaansen
Ina Hulsegge
Dirkjan Schokker
Esther D. Ellen
Bert Klandermans
Marjaneh Taghavi
Claudia Kamphuis
Continuous real-time cow identification by reading ear tags from live-stream video
Frontiers in Animal Science
deep learning
image analysis
precision farming
animal identification
number recognition
title Continuous real-time cow identification by reading ear tags from live-stream video
title_full Continuous real-time cow identification by reading ear tags from live-stream video
title_fullStr Continuous real-time cow identification by reading ear tags from live-stream video
title_full_unstemmed Continuous real-time cow identification by reading ear tags from live-stream video
title_short Continuous real-time cow identification by reading ear tags from live-stream video
title_sort continuous real time cow identification by reading ear tags from live stream video
topic deep learning
image analysis
precision farming
animal identification
number recognition
url https://www.frontiersin.org/articles/10.3389/fanim.2022.846893/full
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