Sheep body automatic measurement based on fusion of color and depth image
Abstrak
Visual livestock measurement techniques offer non-contact operation, high efficiency, and reduced animal stress. However, traditional 2D imaging lacks depth data, while 3D reconstruction faces computational and environmental constraints, limiting real-world applicability. This study presents a novel RGB-D fusion method for efficient sheep morphometric analysis. Using Kinect V2 sensors, top-down and lateral RGB-D data were captured from Dorper sheep. An optimized YOLOv8pose_slimneck framework was used to detect keypoints of body dimension in color images, with depth values derived from aligned RGB-D pairs. Six body dimensions—body length, height, rump height, chest depth, chest width, and rump width—were calculated. On-farm experiment demonstrated that the automated body dimension measurements achieved <5 % error, and subsequent liveweight prediction based on these visual measurements yielded a mean error of 5.3 %. This approach demonstrates strong practical feasibility for implementing precision sheep farming.
Topik & Kata Kunci
Penulis (6)
Lina Zhang
Bin Zhao
Fan Yang
Jue Zhang
Xinhua Jiang
Lin Zhu
Akses Cepat
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- 2025
- Sumber Database
- DOAJ
- DOI
- 10.1016/j.atech.2025.101649
- Akses
- Open Access ✓