How Can AI Help Improve Food Safety?

被引:19
|
作者
Qian, C. [1 ]
Murphy, S., I [2 ]
Orsi, R. H. [1 ]
Wiedmann, M. [1 ]
机构
[1] Cornell Univ, Dept Food Sci, Ithaca, NY 14853 USA
[2] Cornell Univ, Dept Populat Med & Diagnost Sci, Ithaca, NY USA
关键词
machine learning; public health; supply chain; computer vision; predictive; analytics; MACHINE LEARNING APPLICATIONS; SOURCE ATTRIBUTION; COLD CHAIN; ULTRASONIC MEASUREMENTS; LISTERIA-MONOCYTOGENES; FOODBORNE ILLNESS; SUPPLY-CHAIN; CLASSIFICATION; PATHOGENS; SURVEILLANCE;
D O I
10.1146/annurev-food-060721-013815
中图分类号
TS2 [食品工业];
学科分类号
0832 ;
摘要
With advances in artificial intelligence (AI) technologies, the development and implementation of digital food systems are becoming increasingly possible. There is tremendous interest in using different AI applications, such as machine learning models, natural language processing, and computer vision to improve food safety. Possible AI applications are broad and include, but are not limited to, (a) food safety risk prediction and monitoring as well as food safety optimization throughout the supply chain, (b) improved public health systems (e.g., by providing early warning of outbreaks and source attribution), and (c) detection, identification, and characterization of foodborne pathogens. However, AI technologies in food safety lag behind in commercial development because of obstacles such as limited data sharing and limited collaborative research and development efforts. Future actions should be directed toward applying data privacy protection methods, improving data standardization, and developing a collaborative ecosystem to drive innovations in AI applications to food safety.
引用
收藏
页码:517 / 538
页数:22
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