Version 1
: Received: 15 January 2024 / Approved: 15 January 2024 / Online: 16 January 2024 (09:55:16 CET)
How to cite:
Lee, J.G.; Lee, S.S.; Alam, M.; Lee, S.M.; Seong, H.; Park, M.N.; Nguyen, H.; Nguyen, D.T.; Baek, M.K.; Dang, C.G.; Han, S. Body Condition Score Assessment of Dairy Cows Using Deep Neural Network and 3D Imaging. Preprints2024, 2024011156. https://doi.org/10.20944/preprints202401.1156.v1
Lee, J.G.; Lee, S.S.; Alam, M.; Lee, S.M.; Seong, H.; Park, M.N.; Nguyen, H.; Nguyen, D.T.; Baek, M.K.; Dang, C.G.; Han, S. Body Condition Score Assessment of Dairy Cows Using Deep Neural Network and 3D Imaging. Preprints 2024, 2024011156. https://doi.org/10.20944/preprints202401.1156.v1
Lee, J.G.; Lee, S.S.; Alam, M.; Lee, S.M.; Seong, H.; Park, M.N.; Nguyen, H.; Nguyen, D.T.; Baek, M.K.; Dang, C.G.; Han, S. Body Condition Score Assessment of Dairy Cows Using Deep Neural Network and 3D Imaging. Preprints2024, 2024011156. https://doi.org/10.20944/preprints202401.1156.v1
APA Style
Lee, J.G., Lee, S.S., Alam, M., Lee, S.M., Seong, H., Park, M.N., Nguyen, H., Nguyen, D.T., Baek, M.K., Dang, C.G., & Han, S. (2024). Body Condition Score Assessment of Dairy Cows Using Deep Neural Network and 3D Imaging. Preprints. https://doi.org/10.20944/preprints202401.1156.v1
Chicago/Turabian Style
Lee, J.G., Chang Gwon Dang and Seungkyu Han. 2024 "Body Condition Score Assessment of Dairy Cows Using Deep Neural Network and 3D Imaging" Preprints. https://doi.org/10.20944/preprints202401.1156.v1
Abstract
This study presents a novel approach for automating the body condition scoring of dairy cows, leveraging advancements in 3D imaging technology and deep neural networks. The primary objective was to design and implement a system capable of accurately and efficiently assessing the body condition of dairy cows, a critical metric in livestock management for optimizing health and productivity. To achieve this, a 3D camera was employed to capture detailed point cloud data, reconstructing the three-dimensional morphology of individual cows. The obtained data were then fed into a deep neural network, specifically tailored for the task of ranking body condition. The neural network was trained on a diverse dataset of annotated body condition score representing varying degrees of body condition, ensuring robust performance across different physiological states. The results demonstrate the efficacy of the proposed system in automatically and objectively scoring the body condition of dairy cows. The automated process not only expedites the assessment but also reduces the subjectivity associated with manual scoring methods. This innovative approach holds promise for improving the efficiency of dairy farm management by providing timely and accurate body condition assessments. The integration of 3D imaging and deep learning techniques paves the way for future advancements in precision livestock farming, contributing to enhanced animal welfare, optimized production, and sustainable agriculture practices.
Keywords
dairy cow; body condition score; smart farming; deep learning; machine learning; 3D imaging.
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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