Automated system for calving time prediction and cattle classification utilizing trajectory data and movement features
Abstract Accurately predicting the calving time in cattle is essential for optimizing livestock management and ensuring animal welfare. Our research focuses on developing a robust system for calving cattle classification and calving time prediction, utilizing 12-h trajectory data for 20 cattle. Our...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-85932-0 |
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| author | Wai Hnin Eaindrar Mg Thi Thi Zin Pyke Tin Masaru Aikawa Kazuyuki Honkawa Yoichiro Horii |
| author_facet | Wai Hnin Eaindrar Mg Thi Thi Zin Pyke Tin Masaru Aikawa Kazuyuki Honkawa Yoichiro Horii |
| author_sort | Wai Hnin Eaindrar Mg |
| collection | DOAJ |
| description | Abstract Accurately predicting the calving time in cattle is essential for optimizing livestock management and ensuring animal welfare. Our research focuses on developing a robust system for calving cattle classification and calving time prediction, utilizing 12-h trajectory data for 20 cattle. Our system classifies cattle as abnormal (requiring human assistance) or normal (not requiring assistance) and predicts calving times based on their individual behaviors. We employed a tailored YOLOv8 model for efficient and precise cattle detection, effectively filtering out noise such as people and trucks. Our Customized Tracking Algorithm (CTA) maintains continuous identity tracking for each cow, enabling accurate re-identification even during occlusions. To minimize some ID switching errors over extended tracking periods, we integrated IDs optimization in the CTA utilizing Global IDs identification. We extracted and compared three total movement features for classifying cattle as abnormal or normal. For predicting calving times for each cow, we utilized and compared three cumulative movement features. Our system is fully automated, detecting and tracking all 20 cattle continuously for 12 h without manual assistance, and achieving an overall accuracy of 99%. By comparing three features derived from the trajectory tracking data for each point in a frame, we achieve 100%, 95%, 85% accuracy in classifying cattle as abnormal or normal and predict their calving times with a precision of within the next 6 h, within the next 9 h, within the next 8 h, respectively. Our system enables farmers to provide timely assistance, ensuring the health and safety of both the cow and the calf. Furthermore, it aids in optimizing resource allocation and enhancing overall farm efficiency, emphasizing the critical importance of calving time prediction in sustainable livestock farming. |
| format | Article |
| id | doaj-art-f3fe437c979a4cf19f2e1a485658f8e1 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-f3fe437c979a4cf19f2e1a485658f8e12025-01-19T12:23:01ZengNature PortfolioScientific Reports2045-23222025-01-0115112410.1038/s41598-025-85932-0Automated system for calving time prediction and cattle classification utilizing trajectory data and movement featuresWai Hnin Eaindrar Mg0Thi Thi Zin1Pyke Tin2Masaru Aikawa3Kazuyuki Honkawa4Yoichiro Horii5Interdisciplinary Graduate School of Agriculture and Engineering, University of MiyazakiGraduate School of Engineering, University of MiyazakiGraduate School of Engineering, University of MiyazakiOrganization for Learning and Student Development, University of MiyazakiHonkawa RanchCenter for Animal Disease Control, University of MiyazakiAbstract Accurately predicting the calving time in cattle is essential for optimizing livestock management and ensuring animal welfare. Our research focuses on developing a robust system for calving cattle classification and calving time prediction, utilizing 12-h trajectory data for 20 cattle. Our system classifies cattle as abnormal (requiring human assistance) or normal (not requiring assistance) and predicts calving times based on their individual behaviors. We employed a tailored YOLOv8 model for efficient and precise cattle detection, effectively filtering out noise such as people and trucks. Our Customized Tracking Algorithm (CTA) maintains continuous identity tracking for each cow, enabling accurate re-identification even during occlusions. To minimize some ID switching errors over extended tracking periods, we integrated IDs optimization in the CTA utilizing Global IDs identification. We extracted and compared three total movement features for classifying cattle as abnormal or normal. For predicting calving times for each cow, we utilized and compared three cumulative movement features. Our system is fully automated, detecting and tracking all 20 cattle continuously for 12 h without manual assistance, and achieving an overall accuracy of 99%. By comparing three features derived from the trajectory tracking data for each point in a frame, we achieve 100%, 95%, 85% accuracy in classifying cattle as abnormal or normal and predict their calving times with a precision of within the next 6 h, within the next 9 h, within the next 8 h, respectively. Our system enables farmers to provide timely assistance, ensuring the health and safety of both the cow and the calf. Furthermore, it aids in optimizing resource allocation and enhancing overall farm efficiency, emphasizing the critical importance of calving time prediction in sustainable livestock farming.https://doi.org/10.1038/s41598-025-85932-0 |
| spellingShingle | Wai Hnin Eaindrar Mg Thi Thi Zin Pyke Tin Masaru Aikawa Kazuyuki Honkawa Yoichiro Horii Automated system for calving time prediction and cattle classification utilizing trajectory data and movement features Scientific Reports |
| title | Automated system for calving time prediction and cattle classification utilizing trajectory data and movement features |
| title_full | Automated system for calving time prediction and cattle classification utilizing trajectory data and movement features |
| title_fullStr | Automated system for calving time prediction and cattle classification utilizing trajectory data and movement features |
| title_full_unstemmed | Automated system for calving time prediction and cattle classification utilizing trajectory data and movement features |
| title_short | Automated system for calving time prediction and cattle classification utilizing trajectory data and movement features |
| title_sort | automated system for calving time prediction and cattle classification utilizing trajectory data and movement features |
| url | https://doi.org/10.1038/s41598-025-85932-0 |
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