Calf posture recognition using convolutional neural network
Dairy farm management is crucial to maintain the longevity of the farm, and poor dairy youngstock or calf management could lead to gradually deteriorating calf health, which often causes premature death. This was found to be the most neglected part among the management workflows in Malaysia and has...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Tech Science Press
2022
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| Online Access: | http://psasir.upm.edu.my/id/eprint/106932/ http://psasir.upm.edu.my/id/eprint/106932/1/Calf%20Posture%20Recognition%20Using%20Convolutional%20Neural%20Network.pdf |
| _version_ | 1848864850877349888 |
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| author | Tung, Tan Chen Khairuddin, Uswah Shapiai, Mohd Ibrahim Md Nor, Norhariani Hiew, Mark Wen Han Mohd Suhaimie, Nurul Aisyah |
| author_facet | Tung, Tan Chen Khairuddin, Uswah Shapiai, Mohd Ibrahim Md Nor, Norhariani Hiew, Mark Wen Han Mohd Suhaimie, Nurul Aisyah |
| author_sort | Tung, Tan Chen |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Dairy farm management is crucial to maintain the longevity of the farm, and poor dairy youngstock or calf management could lead to gradually deteriorating calf health, which often causes premature death. This was found to be the most neglected part among the management workflows in Malaysia and has caused continuous loss over the recent years. Calf posture recognition is one of the effective methods to monitor calf behaviour and health state, which can be achieved by monitoring the calf behaviours of standing and lying where the former depicts active calf, and the latter, passive calf. Calf posture recognition module is an important component of some automated calf monitoring systems, as the system requires the calf to be in a standing posture before proceeding to the next stage of monitoring, or at the very least, to monitor the activeness of the calves. Calf posture such as standing or resting can easily be distinguished by human eye, however, to be recognized by a machine, it will require more complicated frameworks, particularly one that involves a deep learning neural networks model. Large number of high-quality images are required to train a deep learning model for such tasks. In this paper, multiple Convolutional Neural Network (CNN) architectures were compared, and the residual network (ResNet) model (specifically, ResNet-50) was ultimately chosen due to its simplicity, great performance, and decent inference time. Two ResNet-50 models having the exact same architecture and configuration have been trained on two different image datasets respectively sourced by separate cameras placed at different angle. There were two camera placements to use for comparison because camera placements can significantly impact the quality of the images, which is highly correlated to the deep learning model performance. After model training, the performance for both CNN models were 99.7% and 99.99% accuracies, respectively, and is adequate for a real-time calf monitoring system. |
| first_indexed | 2025-11-15T13:55:22Z |
| format | Article |
| id | upm-106932 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T13:55:22Z |
| publishDate | 2022 |
| publisher | Tech Science Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1069322024-10-17T06:56:44Z http://psasir.upm.edu.my/id/eprint/106932/ Calf posture recognition using convolutional neural network Tung, Tan Chen Khairuddin, Uswah Shapiai, Mohd Ibrahim Md Nor, Norhariani Hiew, Mark Wen Han Mohd Suhaimie, Nurul Aisyah Dairy farm management is crucial to maintain the longevity of the farm, and poor dairy youngstock or calf management could lead to gradually deteriorating calf health, which often causes premature death. This was found to be the most neglected part among the management workflows in Malaysia and has caused continuous loss over the recent years. Calf posture recognition is one of the effective methods to monitor calf behaviour and health state, which can be achieved by monitoring the calf behaviours of standing and lying where the former depicts active calf, and the latter, passive calf. Calf posture recognition module is an important component of some automated calf monitoring systems, as the system requires the calf to be in a standing posture before proceeding to the next stage of monitoring, or at the very least, to monitor the activeness of the calves. Calf posture such as standing or resting can easily be distinguished by human eye, however, to be recognized by a machine, it will require more complicated frameworks, particularly one that involves a deep learning neural networks model. Large number of high-quality images are required to train a deep learning model for such tasks. In this paper, multiple Convolutional Neural Network (CNN) architectures were compared, and the residual network (ResNet) model (specifically, ResNet-50) was ultimately chosen due to its simplicity, great performance, and decent inference time. Two ResNet-50 models having the exact same architecture and configuration have been trained on two different image datasets respectively sourced by separate cameras placed at different angle. There were two camera placements to use for comparison because camera placements can significantly impact the quality of the images, which is highly correlated to the deep learning model performance. After model training, the performance for both CNN models were 99.7% and 99.99% accuracies, respectively, and is adequate for a real-time calf monitoring system. Tech Science Press 2022-09-22 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/106932/1/Calf%20Posture%20Recognition%20Using%20Convolutional%20Neural%20Network.pdf Tung, Tan Chen and Khairuddin, Uswah and Shapiai, Mohd Ibrahim and Md Nor, Norhariani and Hiew, Mark Wen Han and Mohd Suhaimie, Nurul Aisyah (2022) Calf posture recognition using convolutional neural network. Computers, Materials and Continua, 74 (1). pp. 1493-1508. ISSN 1546-2218; ESSN: 1546-2226 https://www.techscience.com/cmc/v74n1/49784 10.32604/cmc.2023.029277 |
| spellingShingle | Tung, Tan Chen Khairuddin, Uswah Shapiai, Mohd Ibrahim Md Nor, Norhariani Hiew, Mark Wen Han Mohd Suhaimie, Nurul Aisyah Calf posture recognition using convolutional neural network |
| title | Calf posture recognition using convolutional neural network |
| title_full | Calf posture recognition using convolutional neural network |
| title_fullStr | Calf posture recognition using convolutional neural network |
| title_full_unstemmed | Calf posture recognition using convolutional neural network |
| title_short | Calf posture recognition using convolutional neural network |
| title_sort | calf posture recognition using convolutional neural network |
| url | http://psasir.upm.edu.my/id/eprint/106932/ http://psasir.upm.edu.my/id/eprint/106932/ http://psasir.upm.edu.my/id/eprint/106932/ http://psasir.upm.edu.my/id/eprint/106932/1/Calf%20Posture%20Recognition%20Using%20Convolutional%20Neural%20Network.pdf |