Deep learning-based analysis of daily activity patterns of farmed dromedary camels
IntroductionThis study addresses the need for automated monitoring solutions to evaluate the daily activity patterns of camels, which is critical for improving animal welfare and farm management practices. By leveraging advanced deep learning techniques, this research aims to identify and analyze fi...
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Animal Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fanim.2024.1445133/full |
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| author | Rama Al-Khateeb Nabil Mansour Nabil Mansour Shaher Bano Mirza Fouad Lamghari |
| author_facet | Rama Al-Khateeb Nabil Mansour Nabil Mansour Shaher Bano Mirza Fouad Lamghari |
| author_sort | Rama Al-Khateeb |
| collection | DOAJ |
| description | IntroductionThis study addresses the need for automated monitoring solutions to evaluate the daily activity patterns of camels, which is critical for improving animal welfare and farm management practices. By leveraging advanced deep learning techniques, this research aims to identify and analyze five key daily activities—sleeping, sitting, standing, eating, and drinking—using video recordings from a camel farm in Fujairah, United Arab Emirates.MethodsThe dataset was collected over two 7-day phases in November and December 2022. In Phase 1, video recordings were analyzed to monitor the activities of two camels and measure the duration of each activity. In Phase 2, the study expanded to include six camels, enabling an evaluation of individual behavioral variations. The YOLOv7 object detection algorithm was used to train and validate the model on images extracted from the recordings, achieving high accuracy in detecting and classifying the defined activities.ResultsThe results showed notable variations in activity patterns between Phases 1 and 2. Average standing time decreased from 9.8 hours (40.8%) to 6.0 hours (25.1%), and sleeping time dropped from 4.3 hours (18.0%) to 2.8 hours (11.7%). Conversely, sitting time increased from 6.2 hours (25.8%) to 9.9 hours (41.5%), and eating time rose from 3.1 hours (12.8%) to 4.6 hours (19.2%). Drinking time remained consistent at an average of 37 minutes (2.6%) across both phases. Activity peaks were observed during early mornings and after 16:00, with midday hours dominated by resting in shaded areas. Evening and nighttime activities primarily included sitting, minimal head movements, and occasional standing or walking.DiscussionThe established deep learning framework demonstrated reliable performance in detecting and analyzing camel activity patterns, offering a practical solution for continuous monitoring and improved farm management. However, further research is recommended to validate the model’s performance across different seasons and environmental conditions to enhance its robustness and adaptability. |
| format | Article |
| id | doaj-art-cefdf16a71914aa9b45f4d3f426fd5be |
| institution | DOAJ |
| issn | 2673-6225 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Animal Science |
| spelling | doaj-art-cefdf16a71914aa9b45f4d3f426fd5be2025-08-20T02:50:00ZengFrontiers Media S.A.Frontiers in Animal Science2673-62252024-12-01510.3389/fanim.2024.14451331445133Deep learning-based analysis of daily activity patterns of farmed dromedary camelsRama Al-Khateeb0Nabil Mansour1Nabil Mansour2Shaher Bano Mirza3Fouad Lamghari4Department of Camel Research, Fujairah Research Centre (FRC), Fujairah, United Arab EmiratesDepartment of Camel Research, Fujairah Research Centre (FRC), Fujairah, United Arab EmiratesDepartment of Theriogenology, Faculty of Veterinary Medicine, Kafrelsheikh University, Kafr El-Sheikh, EgyptDepartment of Camel Research, Fujairah Research Centre (FRC), Fujairah, United Arab EmiratesDepartment of Camel Research, Fujairah Research Centre (FRC), Fujairah, United Arab EmiratesIntroductionThis study addresses the need for automated monitoring solutions to evaluate the daily activity patterns of camels, which is critical for improving animal welfare and farm management practices. By leveraging advanced deep learning techniques, this research aims to identify and analyze five key daily activities—sleeping, sitting, standing, eating, and drinking—using video recordings from a camel farm in Fujairah, United Arab Emirates.MethodsThe dataset was collected over two 7-day phases in November and December 2022. In Phase 1, video recordings were analyzed to monitor the activities of two camels and measure the duration of each activity. In Phase 2, the study expanded to include six camels, enabling an evaluation of individual behavioral variations. The YOLOv7 object detection algorithm was used to train and validate the model on images extracted from the recordings, achieving high accuracy in detecting and classifying the defined activities.ResultsThe results showed notable variations in activity patterns between Phases 1 and 2. Average standing time decreased from 9.8 hours (40.8%) to 6.0 hours (25.1%), and sleeping time dropped from 4.3 hours (18.0%) to 2.8 hours (11.7%). Conversely, sitting time increased from 6.2 hours (25.8%) to 9.9 hours (41.5%), and eating time rose from 3.1 hours (12.8%) to 4.6 hours (19.2%). Drinking time remained consistent at an average of 37 minutes (2.6%) across both phases. Activity peaks were observed during early mornings and after 16:00, with midday hours dominated by resting in shaded areas. Evening and nighttime activities primarily included sitting, minimal head movements, and occasional standing or walking.DiscussionThe established deep learning framework demonstrated reliable performance in detecting and analyzing camel activity patterns, offering a practical solution for continuous monitoring and improved farm management. However, further research is recommended to validate the model’s performance across different seasons and environmental conditions to enhance its robustness and adaptability.https://www.frontiersin.org/articles/10.3389/fanim.2024.1445133/fulldromedary camelsdeep learningactivity patterncamel farmwelfare |
| spellingShingle | Rama Al-Khateeb Nabil Mansour Nabil Mansour Shaher Bano Mirza Fouad Lamghari Deep learning-based analysis of daily activity patterns of farmed dromedary camels Frontiers in Animal Science dromedary camels deep learning activity pattern camel farm welfare |
| title | Deep learning-based analysis of daily activity patterns of farmed dromedary camels |
| title_full | Deep learning-based analysis of daily activity patterns of farmed dromedary camels |
| title_fullStr | Deep learning-based analysis of daily activity patterns of farmed dromedary camels |
| title_full_unstemmed | Deep learning-based analysis of daily activity patterns of farmed dromedary camels |
| title_short | Deep learning-based analysis of daily activity patterns of farmed dromedary camels |
| title_sort | deep learning based analysis of daily activity patterns of farmed dromedary camels |
| topic | dromedary camels deep learning activity pattern camel farm welfare |
| url | https://www.frontiersin.org/articles/10.3389/fanim.2024.1445133/full |
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