Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects
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 edib...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-10-01
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| Series: | Food Chemistry: X |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590157523003036 |
| _version_ | 1797676530511380480 |
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| author | Yanying Zhang Yuanzhong Wang |
| author_facet | Yanying Zhang Yuanzhong Wang |
| author_sort | Yanying Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T22:30:32Z |
| format | Article |
| id | doaj.art-0e34a7878cde4534b3cc6be191df9104 |
| institution | Directory Open Access Journal |
| issn | 2590-1575 |
| language | English |
| last_indexed | 2024-03-11T22:30:32Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Food Chemistry: X |
| spelling | doaj.art-0e34a7878cde4534b3cc6be191df91042023-09-23T05:12:33ZengElsevierFood Chemistry: X2590-15752023-10-0119100860Machine learning applications for multi-source data of edible crops: A review of current trends and future prospectsYanying Zhang0Yuanzhong Wang1Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China; College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming 650500, ChinaMedicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming 650200, China; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2590157523003036Machine learningEdible cropsMulti-source dataData fusion strategyQuality evaluation |
| spellingShingle | Yanying Zhang Yuanzhong Wang Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects Food Chemistry: X Machine learning Edible crops Multi-source data Data fusion strategy Quality evaluation |
| title | Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects |
| title_full | Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects |
| title_fullStr | Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects |
| title_full_unstemmed | Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects |
| title_short | Machine learning applications for multi-source data of edible crops: A review of current trends and future prospects |
| title_sort | machine learning applications for multi source data of edible crops a review of current trends and future prospects |
| topic | Machine learning Edible crops Multi-source data Data fusion strategy Quality evaluation |
| url | http://www.sciencedirect.com/science/article/pii/S2590157523003036 |
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