使用YOLOv8网络和身体参数检测算法进行雅克母牛活体体重估计的动态个体方法
Yingqi Peng1, Zhaoyuan Peng1, Huawei Zou2
1College of Mechanical and Electrical Engineering, Sichuan Agricultural University, Ya'an, China, 625014.
Journal of dairy science
|February 23, 2024
概括
本研究介绍了一种YOLOv8模型,用于从图像中自动估计鱼的活体体重 (LBW). 这种由人工智能驱动的系统能够准确地检测雅克并估计它们的重量,从而节省劳动力并减少牲畜的压力.
科学领域:
- 管理畜牧业的管理.
- 农业中的人工智能
- 动物科学动物科学
背景情况:
- 活体体重 (LBW) 对于监测牲畜生长至关重要.
- 雅克 (Bos grunniens) 由于其野生性质,为LBW测量带来了独特的挑战.
- 准确而非侵入性的LBW估计对于高效的雅克养殖是必要的.
研究的目的:
- 开发和验证用于实时雅克LBW估计的自动化系统.
- 使用YOLOv8进行准确的雅克检测和识别.
- 为了建立图像衍生的身体参数和实际的LBW之间的相关性.
主要方法:
- 采用YOLOv8用于从二维图像中检测和识别雅克.
- 提取雅克体参数用于LBW估计.
- 使用简单的线性回归来对比估计和实际的LBW.
- 使用精度,回忆,mAP50,R2,RMSE和MAPE评估模型性能.
主要成果:
- YOLOv8在正常姿势的雅克中实现了高检测性能 (81.8%的精度,86.0%的回忆率,90.6%的mAP50).
- 雅克的识别准确度非常好 (97.8%的精度,96.4%的回忆,99.0%的mAP50).
- LBW估计模型显示了12个月大的雅克的强有力的结果 (R2=0.96,RMSE=2.43公斤,MAPE=1.69%).
结论:
- 开发的基于YOLOv8的系统可以实时,非侵入性地估计鱼的LBW.
- 这项技术可以显著减少大型雅克养殖中的劳动力和压力.
- 未来的工作重点是改善不同条件和品种的模型通用化.
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