瓶颈
计算机科学
领域(数学)
牲畜
数据科学
主流
透视图(图形)
风险分析(工程)
人工智能
地理
业务
哲学
嵌入式系统
纯数学
林业
数学
神学
作者
Shadi Nayeri,Mehdi Sargolzaei,Dan Tulpan
出处
期刊:Animal Health Research Reviews
[Cambridge University Press]
日期:2019-06-01
卷期号:20 (1): 31-46
被引量:48
标识
DOI:10.1017/s1466252319000148
摘要
Abstract The current livestock management landscape is transitioning to a high-throughput digital era where large amounts of information captured by systems of electro-optical, acoustical, mechanical, and biosensors is stored and analyzed on a daily and hourly basis, and actionable decisions are made based on quantitative and qualitative analytic results. While traditional animal breeding prediction methods have been used with great success until recently, the deluge of information starts to create a computational and storage bottleneck that could lead to negative long-term impacts on herd management strategies if not handled properly. A plethora of machine learning approaches, successfully used in various industrial and scientific applications, made their way in the mainstream approaches for livestock breeding techniques, and current results show that such methods have the potential to match or surpass the traditional approaches, while most of the time they are more scalable from a computational and storage perspective. This article provides a succinct view on what traditional and novel prediction methods are currently used in the livestock breeding field, how successful they are, and how the future of the field looks in the new digital agriculture era.
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