Automated estimation of pose features in broilers using computer vision

I. Fodor; J.E. Doornweerd; B. de Klerk; A.C. Bouwman

doi:10.3920/978-90-8686-940-4_128

Automated estimation of pose features in broilers using computer vision

I. Fodor^*, J.E. Doornweerd, B. de Klerk, A.C. Bouwman

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › Academic

Abstract

The importance of sensor-based phenotyping is increasing in the broiler industry, aiming at improved animal health and welfare, and lower economic losses. We analysed 11 pose features of 87 individual chickens at three ages (day 14, 21, and 33) using video recordings filmed from behind while the broilers walked through a corridor one by one. A pre-trained deep learning model was trained on a limited number of frames (n=181) to adapt it to a new environment for accurate keypoint detection. Extraction of the three poses of interest (double support, left and right steps) was fully automated. Significant (p

Original language	English
Title of host publication	Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP)
Subtitle of host publication	Technical and species orientated innovations in animal breeding, and contribution of genetics to solving societal challenges
Editors	R.F. Veerkamp, Y. de Haas
Place of Publication	Wageningen
Publisher	Wageningen Academic Publishers
Pages	561-564
Number of pages	4
ISBN (Electronic)	9789086869404
DOIs	https://doi.org/10.3920/978-90-8686-940-4_128
Publication status	Published - 2022
Event	World Congress on Genetics Applied to Livestock Production: WCGALP 2022 - Rotterdam, Netherlands Duration: 3 Jul 2022 → 8 Jul 2022

Conference/symposium

Conference/symposium	World Congress on Genetics Applied to Livestock Production: WCGALP 2022
Country/Territory	Netherlands
City	Rotterdam
Period	3/07/22 → 8/07/22

Access to Document

10.3920/978-90-8686-940-4_128Licence: CC BY

https://edepot.wur.nl/584363Licence: CC BY

Automated estimation of pose features in broilers using computer vision
Fodor, I. (Speaker), Doornweerd, J. E. (Contributor), de Klerk, B. (Contributor) & Bouwman, A. (Contributor)
4 Jul 2022
Activity: Talk/presentation/lecture › Oral presentation › Academic

AF-16022 Breed4Food II (BO-63-001-009, BO-47-001-021, BO-22.04-025-001, BO-22.04-011-001, BO-22.02-011-001)
Veerkamp, R. (Project Leader)
1/01/14 → 31/12/21
Project: LVVN project

Cite this

Fodor, I., Doornweerd, J. E., de Klerk, B., & Bouwman, A. C. (2022). Automated estimation of pose features in broilers using computer vision. In R. F. Veerkamp, & Y. de Haas (Eds.), Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP): Technical and species orientated innovations in animal breeding, and contribution of genetics to solving societal challenges (pp. 561-564). Article 128 Wageningen Academic Publishers. https://doi.org/10.3920/978-90-8686-940-4_128

Fodor, I. ; Doornweerd, J.E. ; de Klerk, B. et al. / Automated estimation of pose features in broilers using computer vision. Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP): Technical and species orientated innovations in animal breeding, and contribution of genetics to solving societal challenges. editor / R.F. Veerkamp ; Y. de Haas. Wageningen : Wageningen Academic Publishers, 2022. pp. 561-564

@inproceedings{8e1b9be149404bfd9961fbbb82fa159a,

title = "Automated estimation of pose features in broilers using computer vision",

abstract = "The importance of sensor-based phenotyping is increasing in the broiler industry, aiming at improved animal health and welfare, and lower economic losses. We analysed 11 pose features of 87 individual chickens at three ages (day 14, 21, and 33) using video recordings filmed from behind while the broilers walked through a corridor one by one. A pre-trained deep learning model was trained on a limited number of frames (n=181) to adapt it to a new environment for accurate keypoint detection. Extraction of the three poses of interest (double support, left and right steps) was fully automated. Significant (p",

author = "I. Fodor and J.E. Doornweerd and {de Klerk}, B. and A.C. Bouwman",

year = "2022",

doi = "10.3920/978-90-8686-940-4_128",

language = "English",

pages = "561--564",

editor = "R.F. Veerkamp and {de Haas}, Y.",

booktitle = "Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP)",

publisher = "Wageningen Academic Publishers",

address = "Netherlands",

note = "World Congress on Genetics Applied to Livestock Production: WCGALP 2022 ; Conference date: 03-07-2022 Through 08-07-2022",

}

Fodor, I, Doornweerd, JE, de Klerk, B & Bouwman, AC 2022, Automated estimation of pose features in broilers using computer vision. in RF Veerkamp & Y de Haas (eds), Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP): Technical and species orientated innovations in animal breeding, and contribution of genetics to solving societal challenges., 128, Wageningen Academic Publishers, Wageningen, pp. 561-564, World Congress on Genetics Applied to Livestock Production: WCGALP 2022, Rotterdam, Netherlands, 3/07/22. https://doi.org/10.3920/978-90-8686-940-4_128

Automated estimation of pose features in broilers using computer vision. / Fodor, I.; Doornweerd, J.E.; de Klerk, B. et al.
Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP): Technical and species orientated innovations in animal breeding, and contribution of genetics to solving societal challenges. ed. / R.F. Veerkamp; Y. de Haas. Wageningen: Wageningen Academic Publishers, 2022. p. 561-564 128.

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › Academic

TY - GEN

T1 - Automated estimation of pose features in broilers using computer vision

AU - Fodor, I.

AU - Doornweerd, J.E.

AU - de Klerk, B.

AU - Bouwman, A.C.

PY - 2022

Y1 - 2022

N2 - The importance of sensor-based phenotyping is increasing in the broiler industry, aiming at improved animal health and welfare, and lower economic losses. We analysed 11 pose features of 87 individual chickens at three ages (day 14, 21, and 33) using video recordings filmed from behind while the broilers walked through a corridor one by one. A pre-trained deep learning model was trained on a limited number of frames (n=181) to adapt it to a new environment for accurate keypoint detection. Extraction of the three poses of interest (double support, left and right steps) was fully automated. Significant (p

AB - The importance of sensor-based phenotyping is increasing in the broiler industry, aiming at improved animal health and welfare, and lower economic losses. We analysed 11 pose features of 87 individual chickens at three ages (day 14, 21, and 33) using video recordings filmed from behind while the broilers walked through a corridor one by one. A pre-trained deep learning model was trained on a limited number of frames (n=181) to adapt it to a new environment for accurate keypoint detection. Extraction of the three poses of interest (double support, left and right steps) was fully automated. Significant (p

U2 - 10.3920/978-90-8686-940-4_128

DO - 10.3920/978-90-8686-940-4_128

M3 - Conference paper

SP - 561

EP - 564

BT - Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP)

A2 - Veerkamp, R.F.

A2 - de Haas, Y.

PB - Wageningen Academic Publishers

CY - Wageningen

T2 - World Congress on Genetics Applied to Livestock Production: WCGALP 2022

Y2 - 3 July 2022 through 8 July 2022

ER -

Fodor I, Doornweerd JE, de Klerk B, Bouwman AC. Automated estimation of pose features in broilers using computer vision. In Veerkamp RF, de Haas Y, editors, Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP): Technical and species orientated innovations in animal breeding, and contribution of genetics to solving societal challenges. Wageningen: Wageningen Academic Publishers. 2022. p. 561-564. 128 doi: 10.3920/978-90-8686-940-4_128

Automated estimation of pose features in broilers using computer vision

Abstract

Conference/symposium

Access to Document

Fingerprint

Activities

Automated estimation of pose features in broilers using computer vision

Projects

AF-16022 Breed4Food II (BO-63-001-009, BO-47-001-021, BO-22.04-025-001, BO-22.04-011-001, BO-22.02-011-001)

Cite this