Article

A computer vision-based approach for behavior recognition of gestating sows fed different fiber levels during high ambient temperature

Payam Hosseinzadeh kasani1, Seung Min Oh1, Sang Hun Ha1, HyungMin Jun2, KyuHyun Park1, Han Seo Ko1, YoHan Choi3, Jo Eun Kim3, JungWoo Choi1, Eun Seok Cho3,*, Jin Soo Kim1,4,**
Author Information & Copyright
1College of Animal Life Sciences, Kangwon National University, , Chuncheon 24341, Korea.
2Devision of Mechanical System Engineering, Jeonbuk National University, 567, Baekje-daero, Deokjin-gu,, Jeonju 54896, Korea.
3Swine Division, National Institute of Animal Science, Rural Development Administration, Cheonan 31000, Korea.
4Department of Bio-health Convergence, Kangwon National University,, Chuncheon 24341, Korea.
**Corresponding Author: Eun Seok Cho, Swine Division, National Institute of Animal Science, Rural Development Administration, Cheonan 31000, Korea, Republic of. E-mail: segi0486@korea.kr.
**Corresponding Author: Jin Soo Kim, College of Animal Life Sciences, Kangwon National University, , Chuncheon 24341, Korea, Republic of. Department of Bio-health Convergence, Kangwon National University,, Chuncheon 24341, Korea, Republic of. E-mail: kjs896@kangwon.ac.kr.

© Copyright 2021 Korean Society of Animal Science and Technology. This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Received: Dec 07, 2020; Revised: Dec 30, 2020; Accepted: Dec 30, 2020

Published Online: Jan 04, 2021

Abstract

The objectives of this study were to evaluate convolutional neural network models and computer vision techniques for the classification of swine posture with high accuracy and to use the derived result in the investigation of the effect of dietary fiber level on the behavioral characteristics of the pregnant sow under low and high ambient temperatures during the last stage of gestation. A total of 27 crossbred sows (Yorkshire × Landrace; average body weight, 192.2 ± 4.8 kg) were assigned to three treatments in a randomized complete block design during the last stage of gestation (days 90 to 114). The sows in group 1 were fed a 3% fiber diet under neutral ambient temperature; the sows in group 2 were fed a diet with 3% fiber under high ambient temperature (HT); the sows in group 3 were fed a 6% fiber diet under HT. Eight popular deep learning-based feature extraction frameworks (DenseNet121, DenseNet201, InceptionResNetV2, InceptionV3, MobileNet, VGG16, VGG19, and Xception) used for automatic swine posture classification were selected and compared using the swine posture image dataset that was constructed under real swine farm conditions. The neural network models showed excellent performance on previously unseen data (ability to generalize). The DenseNet121 feature extractor achieved the best performance with 99.83% accuracy, and both DenseNet201 and MobileNet showed an accuracy of 99.77% for the classification of the image dataset. The behavior of sows classified by the DenseNet121 feature extractor showed that the HT in our study reduced (<italic>p</italic> &lt; 0.05) the standing behavior of sows and also has a tendency to increase (<italic>p</italic> = 0.082) lying behavior. High dietary fiber treatment tended to increase (<italic>p</italic> = 0.064) lying and decrease (<italic>p</italic> &lt; 0.05) the standing behavior of sows, but there was no change in sitting under HT conditions.

Keywords: Convolutional Neural Network; Dietary Fiber; Heat Stress; Machine Learning; Sows


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