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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Autor*in:	John W.M. Bastiaansen [verfasserIn] Ina Hulsegge [verfasserIn] Dirkjan Schokker [verfasserIn] Esther D. Ellen [verfasserIn] Bert Klandermans [verfasserIn] Marjaneh Taghavi [verfasserIn] Claudia Kamphuis [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	deep learning image analysis precision farming animal identification number recognition

Übergeordnetes Werk:	In: Frontiers in Animal Science - Frontiers Media S.A., 2021, 3(2022)
Übergeordnetes Werk:	volume:3 ; year:2022

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fanim.2022.846893

Katalog-ID:	DOAJ083342303

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callnumber-first	S - Agriculture
author	John W.M. Bastiaansen
spellingShingle	John W.M. Bastiaansen misc SF600-1100 misc deep learning misc image analysis misc precision farming misc animal identification misc number recognition misc Veterinary medicine Continuous real-time cow identification by reading ear tags from live-stream video
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topic_title	SF600-1100 Continuous real-time cow identification by reading ear tags from live-stream video deep learning image analysis precision farming animal identification number recognition
topic	misc SF600-1100 misc deep learning misc image analysis misc precision farming misc animal identification misc number recognition misc Veterinary medicine
topic_unstemmed	misc SF600-1100 misc deep learning misc image analysis misc precision farming misc animal identification misc number recognition misc Veterinary medicine
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title	Continuous real-time cow identification by reading ear tags from live-stream video
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author_browse	John W.M. Bastiaansen Ina Hulsegge Dirkjan Schokker Esther D. Ellen Bert Klandermans Marjaneh Taghavi Claudia Kamphuis
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title_sort	continuous real-time cow identification by reading ear tags from live-stream video
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title_auth	Continuous real-time cow identification by reading ear tags from live-stream video
abstract	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.
abstractGer	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.
abstract_unstemmed	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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title_short	Continuous real-time cow identification by reading ear tags from live-stream video
url	https://doi.org/10.3389/fanim.2022.846893 https://doaj.org/article/b0e6b1b9a6a74b2590226f54ec9c4371 https://www.frontiersin.org/articles/10.3389/fanim.2022.846893/full https://doaj.org/toc/2673-6225
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up_date	2024-07-03T16:55:34.742Z
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Continuous real-time cow identification by reading ear tags from live-stream video

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