A computer vision-based approach for behavior recognition of gestating sows fed different fiber levels during high ambient temperature
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...
| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Korean Society of Animal Sciences and Technology
2021-03-01
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| Series: | Journal of Animal Science and Technology |
| Subjects: | |
| Online Access: | http://www.ejast.org/archive/view_article?pid=jast-63-2-367 |
| Summary: | 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 (p < 0.05) the standing behavior
of sows and also has a tendency to increase (p = 0.082) lying
behavior. High dietary fiber treatment tended to increase (p =
0.064) lying and decrease (p < 0.05) the standing
behavior of sows, but there was no change in sitting under HT conditions. |
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| ISSN: | 2672-0191 2055-0391 |