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Application progress of machine learning in agricultural product integrity monitoring and risk prediction
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Ze-Bin LIU1, 2, 3, Yu-Feng GAO1, 2, Xiao-Chu CHEN1, 2, Min-Xing HUANG1, 2, 3, Wei QIN3, *
Journal of Food Safety & Quality | 2025, 16(15) : 126 - 133
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Journal of Food Safety & Quality | 2025, 16(15): 126-133
Special Topic: Food Safety Risk Assessment and Risk Monitoring
Application progress of machine learning in agricultural product integrity monitoring and risk prediction
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Ze-Bin LIU1, 2, 3, Yu-Feng GAO1, 2, Xiao-Chu CHEN1, 2, Min-Xing HUANG1, 2, 3, Wei QIN3, *
Affiliations
  • 1 Institute of Biological and Medical Engineering, Guangdong Academy of Sciences, Guangzhou 510316, China
  • 2 Research Center for Sugarcane Industry Engineering Technology of Light Industry of China, Guangzhou 510316, China
  • 3 Management College, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Published: 2025-08-15 doi: 10.19812/j.cnki.jfsq11-5956/ts.20250226006
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In the face of global population growth and agricultural production pressure, as well as the serious challenges of agricultural product quality and safety issues, the traditional methods of agricultural product integrity monitoring and risk prediction have become insufficient. The rapid development of machine learning technology provides new solution ideas for agricultural product integrity monitoring and risk prediction. This paper systematically summarized the applications of machine learning technology in agricultural product safety risk monitoring (including physical, chemical and biological risks), agricultural product authenticity and traceability assurance, and agricultural product risk assessment prediction based on historical data. Machine learning technology undoubtedly improves the efficiency of agricultural product integrity monitoring effectively, realizes early detection and prevention of risks, and provides new solution for constructing a safer and more reliable food supply chain provides new solutions. Although these applications show great promise, there are still challenges to artificial intelligence in the field of agricultural produce integrity monitoring and risk prediction. Based on summarizing the literature, this paper further explored the prospects and directions of this trend, and presented the importance of machine learning model interpretability and trust issues, as well as problems in data acquisition and use, to improve the application of machine learning in agricultural product integrity monitoring and risk prediction.

agricultural product integrity  /  risk prediction  /  artificial intelligence  /  machine learning
Ze-Bin LIU, Yu-Feng GAO, Xiao-Chu CHEN, Min-Xing HUANG, Wei QIN. Application progress of machine learning in agricultural product integrity monitoring and risk prediction[J]. Journal of Food Safety & Quality, 2025 , 16 (15) : 126 -133 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20250226006
Year 2025 volume 16 Issue 15
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doi: 10.19812/j.cnki.jfsq11-5956/ts.20250226006
  • Receive Date:2025-02-26
  • Online Date:2026-01-09
  • Published:2025-08-15
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  • Received:2025-02-26
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Affiliations
    1 Institute of Biological and Medical Engineering, Guangdong Academy of Sciences, Guangzhou 510316, China
    2 Research Center for Sugarcane Industry Engineering Technology of Light Industry of China, Guangzhou 510316, China
    3 Management College, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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