Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Pose Estimation of Sow and Piglets During Free Farrowing Using Deep Learning

Fahimeh Farahnakian
* ,
Stefan Björkman
ORCID logo ,
Farshad Farahnakian
,
Victor Bloch
,
Matti Pastell
ORCID logo ,
Jukka Heikkonen
Version 1 : Received: 26 April 2023 / Approved: 27 April 2023 / Online: 27 April 2023 (04:19:46 CEST)

A peer-reviewed article of this Preprint also exists.

Farahnakian, F.; Farahnakian, F.; Björkman, S.; Bloch, V.; Pastell, M.; Heikkonen, J. Pose Estimation of Sow and Piglets during Free Farrowing Using Deep Learning. Journal of Agriculture and Food Research 2024, 101067, doi:10.1016/j.jafr.2024.101067. Farahnakian, F.; Farahnakian, F.; Björkman, S.; Bloch, V.; Pastell, M.; Heikkonen, J. Pose Estimation of Sow and Piglets during Free Farrowing Using Deep Learning. Journal of Agriculture and Food Research 2024, 101067, doi:10.1016/j.jafr.2024.101067.

Abstract

Automatic and real-time pose estimation is important in monitoring animal behavior, health and welfare. In this paper, we utilized pose estimation for monitoring farrowing process to prevent piglet mortality and preserve the health and welfare of sow. State-of-the-art Deep Learning (DL) methods have lately been used for animal pose estimation. The aim of this paper was to probe the generalization ability of five common DL networks (ResNet50, ResNet101, MobileNet, EfficientNet and DLCRNet) for sow and piglet pose estimation. These architectures predict body parts of several piglets and the sow directly from input video sequences. Real farrowing data from a commercial farm was used for training and validation of the proposed networks. The experimental results demonstrated that MobileNet was able to detect seven body parts of the sow with median test error of 0.61 pixels.

Keywords

deep learning; convolutional neural networks; livestock; pose estimation; animal behavior

Subject

Computer Science and Mathematics, Computer Vision and Graphics

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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