基于YOLO架构的自动分类系统,用于乳牛的身体状况评分
Emre Dandıl1, Kerim Kürşat Çevik2, Mustafa Boğa3
1Department of Computer Engineering, Faculty of Engineering, Bilecik Şeyh Edebali University, Bilecik 11230, Turkey.
Veterinary sciences
|September 27, 2024
概括
这项研究引入了一种人工智能系统,用于使用YOLOv8x.自动分类乳牛身体状况得分 (BCS). 该系统准确评估牛的福利和健康状况,有助于早期发现代谢问题并改善农场管理.
科学领域:
- 动物科学动物科学
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 身体状况评分 (BCS) 对奶牛的福利至关重要,但传统的评估依赖于主观的专家观察.
- 在BCS中的偏差可以导致代谢疾病,降低生产率,以及乳牛群的经济损失.
研究的目的:
- 开发和评估一个自动化系统,使用深度学习对奶牛BCS进行分类.
- 提高奶牛养殖中BCS评估的准确性和效率.
主要方法:
- 创建了一个原始的牛图像数据集 (霍尔斯坦和Simmental品种),分为五个BCS类:衰弱,贫穷,好,脂肪和肥胖.
- 图像分类使用了YOLOv8x深度学习架构.
- 在准备好的数据集上进行了实验分析.
主要成果:
- 在测试组中的126个牛图像中,YOLOv8x系统正确对102个牛的BCS进行了分类.
- 在所有BCS类中,两种品种的平均精度为0.81.
- 精度回忆曲线下的平均面积为0.87.7.
结论:
- 拟议的自动化BCS分类系统能够准确和快速地监测奶牛的身体状况.
- 这种工具可以帮助生产决策者,特别是在早期的哺乳期,以减轻负能量平衡和加强群体管理.
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