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Updated: Feb 9, 2026

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基于图像处理的自动牙分割和羊的年龄估计,使用深度学习
Pınar Cihan1, Ali Yıldız1, Abdulsamet Baysan1
1Department of Computer Engineering, Faculty of Corlu Engineering, Tekirdağ Namık Kemal University, Tekirdağ Türkiye.
概括
这项研究引入了一种自动化系统,用于使用牙图像和深度学习来估计绵羊的年龄. 效率NetB0模型实现了95%的准确性,改善了精密畜牧业.
科学领域:
- 兽医医学 兽医医学 兽医医学
- 计算机科学 计算机科学
- 农业科学 农业科学
背景情况:
- 准确的羊年龄确定对于畜牧管理至关重要,影响肉质,繁殖和市场价值.
- 传统的年龄估计方法依赖于主观的牙科检查,导致不一致和错误.
- 需要自动化,客观和可重复的系统来估计羊的年龄.
研究的目的:
- 利用深度学习和图像处理开发一种完全自动化的羊年龄估计框架.
- 为此任务比较各种卷积神经网络 (CNN) 架构的性能.
- 创建一个用户友好的界面,用于农业的实际应用.
主要方法:
- 一个深度学习框架,利用图像处理从牙部图像进行羊年龄估计.
- 数据增强技术以解决阶级不平衡并改善模型概括.
- 使用You Only Look Once版本8 (YOLOv8) 的图像细分来隔离相关的牙区域.
- 通过转移学习对多种CNN模型 (VGG16,ResNet50,EfficientNetB0,MobileNetV2,Xception,BasicCNN) 的评估和微调.
- 开发和部署一个容器化的图形用户界面 (GUI),以实现可访问性.
主要成果:
- EfficientNetB0模型表现出最高的性能,总体准确率达到95%.
- 具体准确率包括3-12个月和2-3岁年龄组的97%,1-1,5岁年龄组的92%和1.5至2岁年龄组的93%.
- 自动细分和转移学习的结合大大提高了模型的概括性和分类准确性.
结论:
- 拟议的框架为估计绵羊年龄提供了一个强大,自动化和可扩展的解决方案.
- 这种自动化系统减少了对手工评估的依赖,有助于精密畜牧业.
- 集成先进技术和用户友好的界面支持知情决策和潜在的广泛采用在农业实践.
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