Recent advances of machine learning in the geographical origin traceability of food and agro‐products: A review

可追溯性 计算机科学 食品 可解释性 农业 跟踪(心理语言学) 过程(计算) 人工智能 地理 食品科学 化学 语言学 操作系统 软件工程 哲学 考古
作者
Jiali Li,Jianping Qian,Jinyong Chen,Luis Ruiz-García,Chen Dong,Qian Chen,Zihan Liu,Pengnan Xiao,Zhiyao Zhao
出处
期刊:Comprehensive Reviews in Food Science and Food Safety [Wiley]
卷期号:24 (1) 被引量:4
标识
DOI:10.1111/1541-4337.70082
摘要

Abstract The geographical origin traceability of food and agro‐products has been attracted worldwide. Especially with the rise of machine learning (ML) technology, it provides cutting‐edge solutions to erstwhile intractable issues to identify the origin of food and agro‐products. By utilizing advanced algorithms, ML can extract feature information of food and agro‐products that is closely related to origin and, more accurately, identify and trace their origins, which is of great significance to the entire food and agriculture industry. This paper provides a comprehensive overview of the state‐of‐the‐art applications of ML in the geographical origin traceability of food and agro‐products. First, commonly used ML methods are summarized. The paper then outlines the whole process of preparation for modeling, model training as well as model evaluation for building traceability models–based ML. Finally, recent applications of ML combined with different traceability techniques in the field of food and agro‐products are revisited. Although ML has made many achievements in solving the geographical origin traceability problem of food and agro‐products, it still has great development potential. For example, the application of ML is yet insufficient in the geographical origin traceability using DNA or computer vision techniques. The ability of ML to predict the geographical origin of food and agro‐products can be further improved, for example, by increasing model interpretability, incorporating data fusion strategies, and others.
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