Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects
文献类型： 外文期刊
第一作者： Zhang, Yanying
作者： Zhang, Yanying;Wang, Yuanzhong;Zhang, Yanying
作者机构：
关键词： Machine learning; Edible crops; Multi-source data; Data fusion strategy; Quality evaluation
期刊名称：FOOD CHEMISTRY-X （ 影响因子：6.1； 五年影响因子：6.4 ）
ISSN： 2590-1575
年卷期： 2023 年 19 卷
页码：
收录情况： SCI
摘要： 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.
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