使用声音信号和机器学习对乳牛的和反进行分类
Saman Abdanan Mehdizadeh1, Mohsen Sari2, Hadi Orak1
1Department of Mechanics of Biosystems Engineering, Faculty of Agricultural Engineering and Rural Development, Agricultural Sciences and Natural Resources University of Khuzestan, Ahvaz 63417-73637, Iran.
Animals : an open access journal from MDPI
|September 28, 2023
概括
这项研究开发了一种新方法来分类奶牛的下巴运动,在理解他们的饮食行为方面实现了高准确性,并改善了料管理,以改善动物福利和生产力.
科学领域:
- 动物科学动物科学
- 农业工程 农业工程
- 生物声学是一种生物声学.
背景情况:
- 了解奶牛的养行为对于优化营养和生产率至关重要.
- 食期间的部运动为饮食摄入量和料加工提供了关键的见解.
- 准确地对部运动进行分类,可以帮助识别营养缺陷,改善养策略.
研究的目的:
- 引入一种新的方法来分类乳牛部运动.
- 分析子运动在食不同颗粒大小的小麦和草.
- 评估各种机器学习分类器对此任务的有效性.
主要方法:
- 机运动的声音信号被记录下来,并使用短时间里埃变换转化为图像.
- 使用已知的图像分析方法 (GLCM,SGLDM,GLRLM,GLDM) 提取了纹理特征.
- 遗传算法 (GA) 用于特征选择,然后使用六种不同的算法进行分类 (Naive Bayes,k-NN,SVM,决策树,MLP,k-Means).
主要成果:
- 该研究成功地分类了四个不同的部运动类别:咬伤,独家,咬组合和独家分类.
- 所有测试的分类器都实现了高分类精度,支持矢量机 (SVM) 达到95.9%.
- 该方法证明了基于声信号准确分析牛养行为的潜力.
结论:
- 开发的方法为畜牧管理人员提供了一种有价值的工具,以评估乳牛的营养和养做法.
- 准确分类下巴运动可以提高对饮食模式的理解,有助于识别健康问题或缺陷.
- 这种方法可以带来更好的养策略,减少浪费,改善牛奶牛的整体福利和生产力.
相关概念视频
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