通过低频超声成像对母猪怀孕进行深度学习增强诊断
Tae-Kyeong Kim1, Yo-Han Choi2, Jun-Seon Hong2
1Department of Data Science, Kangwon National University, Chuncheon 24341, Republic of Korea.
Animals : an open access journal from MDPI
|February 13, 2025
概括
深度学习通过使用低频超声图像来改善母猪怀孕诊断. 这项技术通过为畜牧管理提供准确和快速的结果来提高农场的利能力.
科学领域:
- 兽医医学 兽医医学 兽医医学
- 农业技术 农业技术
- 人工智能的人工智能
背景情况:
- 准确的母猪怀孕诊断对于农场的利能力至关重要,因为猪肉需求不断增加.
- 目前的低频超声波方法提供的图像分辨率低于最佳,阻碍了诊断准确度.
- 需要先进的技术来改善母猪在畜牧业中的怀孕检测.
研究的目的:
- 开发和评估深度学习模型,以改善母猪怀孕诊断.
- 在低频超声波图像上分析ViT-H,ConvNeXt-xlarge和Xception模型的性能.
- 用AutoAugment评估数据增强对模型稳定性和准确性的影响.
主要方法:
- 开发了三种深度学习分类模型:ViT-H,ConvNeXt-xlarge和Xception.
- 应用AutoAugment用于数据增强,以增加数据集大小和模型稳定性.
- 使用曲线下的面积 (AUC) 值对模型性能进行比较分析.
主要成果:
- 深度学习模型显示,母猪怀孕的诊断性能得到了改善.
- 自动增强显著提高了模型的稳定性和准确性.
- 在评估模型中,AUC值为0.865,0.856和0.866.
结论:
- 深度学习为畜牧场的母猪怀孕诊断提供了可行和有效的解决方案.
- 这些发现支持将人工智能整合到农业技术中,以改善畜牧业.
- 这项研究提出了一种可扩展和有效的方法来管理母猪怀孕.
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