Zhang Yanying, Wang Yuanzhong
Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China.
College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming 650500, China.
Food Chem X. 2023 Sep 3;19:100860. doi: 10.1016/j.fochx.2023.100860. eCollection 2023 Oct 30.
The quality and safety of edible crops are key links inseparable from human health and nutrition. In the era of rapid development of artificial intelligence, using it to mine multi-source information on edible crops provides new opportunities for industrial development and market supervision of edible crops. This review comprehensively summarized the applications of multi-source data combined with machine learning in the quality evaluation of edible crops. Multi-source data can provide more comprehensive and rich information from a single data source, as it can integrate different data information. Supervised and unsupervised machine learning is applied to data analysis to achieve different requirements for the quality evaluation of edible crops. Emphasized the advantages and disadvantages of techniques and analysis methods, the problems that need to be overcome, and promising development directions were proposed. To monitor the market in real-time, the quality evaluation methods of edible crops must be innovated.
食用作物的质量和安全是与人类健康和营养密不可分的关键环节。在人工智能快速发展的时代，利用其挖掘食用作物的多源信息为食用作物的产业发展和市场监管提供了新机遇。本文综述全面总结了多源数据结合机器学习在食用作物质量评价中的应用。多源数据能够整合不同的数据信息，相较于单一数据源可提供更全面丰富的信息。监督式和非监督式机器学习应用于数据分析，以实现对食用作物质量评价的不同要求。强调了技术和分析方法的优缺点、需克服的问题，并提出了有前景的发展方向。为实时监测市场，必须创新食用作物的质量评价方法。
