摘要
面对全球人口增长和农业生产压力,以及农产品质量安全问题的严峻挑战,传统的农产品完整性监测和风险预测方法已显不足。机器学习技术的飞速发展为农产品完整性监测和风险预测提供了新的解决思路。本文系统总结了机器学习技术在农产品安全风险监测(包括物理性、化学性和生物性风险)、农产品真实性和可追溯性保障,以及基于历史数据的农产品风险评估预测等方面的应用。机器学习技术无疑有效提高了农产品完整性监测效率,实现风险的早期发现和预防,为构建更安全、可靠的食品供应链提供了新的解决方案。虽然这些应用展示了巨大的前景,但在农产品完整性监测和风险预测领域人工智能化仍面临挑战。在总结文献的基础上,本文进一步探讨了这一趋势的前景和方向,提出了机器学习模型可解释性与信任问题的重要性,以及数据的获取和使用方面存在的问题,以改进机器学习在农产品完整性监测和风险预测中的应用。
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.
作者
刘泽槟
高裕锋
陈晓初
黄敏兴
秦伟
LIU Ze-Bin;GAO Yu-Feng;CHEN Xiao-Chu;HUANG Min-Xing;QIN Wei(Institute of Biological and Medical Engineering,Guangdong Academy of Sciences,Guangzhou 510316,China;Research Center for Sugarcane Industry Engineering Technology of Light Industry of China,Guangzhou 510316,China;Management College,Zhongkai University of Agriculture and Engineering,Guangzhou 510225,China)
出处
《食品安全质量检测学报》
2025年第15期126-133,共8页
Journal of Food Safety and Quality
基金
广东省自然科学基金面上项目(2023A1515010998)。
关键词
农产品完整性
风险预测
人工智能
机器学习
agricultural product integrity
risk prediction
artificial intelligence
machine learning
