注意模块纳入转移学习授权的基于深度学习的模型,用于对表型相似的热带牛品种 (Bos indicus) 的分类
Radhika Warhade1, Indu Devi2, Naseeb Singh3
1Livestock Production Management Division, ICAR-National Dairy Research Institute, Karnal, 132001, Haryana, India.
Tropical animal health and production
|July 2, 2024
概括
这项研究开发了一种深度学习模型,用于准确识别乳牛品种,特别是区分萨希瓦尔和红辛迪奶牛. 该模型实现了高精度,减少了农民对品种分类专家知识的依赖.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 动物科学动物科学
背景情况:
- 准确的乳牛品种识别对于有效的牛群管理和遗传改进至关重要.
- 现型相似的品种对传统的识别方法构成挑战,影响生产力.
- 开发自动化系统可以帮助农民优化牲畜管理策略.
研究的目的:
- 开发和评估基于卷积神经网络 (CNN) 的模型,用于识别萨希瓦尔和红辛迪牛品种.
- 通过实施图像细分和使用多视图输入来提高分类准确性.
- 为品种识别提供自动化解决方案,减少对专家评估的依赖.
主要方法:
- 一个CNN模型被用来从背景对牛像素进行细分,从而创建面具图像.
- 多输入CNN模型使用面具牛图像的正面和侧面视图 (原始和灰度) 进行训练.
- 具有预训练重量的MobileNet和EfficientNet架构的变体作为分类的骨干.
主要成果:
- 该细分模型实现了81.75%的交叉跨欧盟 (IoU) 和85.26%的F1得分.
- 基于EfficientNet的CNN模型表现出卓越的性能,分类准确度在84.0%至86.0%之间.
- 移动网络V2和移动网络V3模型达到82.0%的准确性,F1总体得分超过了品种分类的83.0%.
结论:
- 深度学习模型,特别是利用EfficientNet骨干的CNN,对于识别表型相似的牛品种是有效的.
- 图像细分和多视图输入策略显著提高了品种分类的准确性.
- 开发的自动化系统为基于专家的识别提供了可靠的替代方案,有利于奶牛养殖者.
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