机器学习在农田土壤污染识别、修复和风险评估中的应用研究进展

机器学习在农田土壤污染识别、修复和风险评估中的应用研究进展李杏桢,林汉森,邱少健,林庆祺,叶龙,麦粤帮,吴培浩,倪卓彪,仇荣亮引用本文:李杏桢, 林汉森, 邱少健, 等. 机器学习在农田土壤污染识别、修复和风险评估中的应用研究进展[J]. 农业资源与环境学报,2025, 42(5): 1125-1139.在线阅读 View online: https://doi.org/10.13254/j.jare.2024.0776您可能感兴趣的其他文章Articles you may be interested in镉污染农田原位钝化修复效果及其机理研究进展冯敬云,聂新星,刘波,李方敏,杨利农业资源与环境学报. 2021, 38(5): 764-777 https://doi.org/10.13254/j.jare.2020.0521重金属污染农田原位钝化修复材料研究进展吴霄霄,曹榕彬,米长虹,林大松,王天恕农业资源与环境学报. 2019, 36(3): 253-263 https://doi.org/10.13254/j.jare.2018.0101某炼钢厂周边农田土壤重金属污染状况的调查与评价谢团辉,胡聪,陈炎辉,徐芹磊,王果农业资源与环境学报. 2018, 35(2): 155-160 https://doi.org/10.13254/j.jare.2018.0014安徽省某市农田土壤与农产品重金属污染评价岳蛟,叶明亮,杨梦丽,崔俊义,马友华农业资源与环境学报. 2019, 36(1): 53-61 https://doi.org/10.13254/j.jare.2017.0336贝壳类废弃物用于钝化土壤重金属的研究进展张冉,高宝林,郭丽莉,吴佳俐,彭宇涛,陈清农业资源与环境学报. 2021, 38(5): 787-796 https://doi.org/10.13254/j.jare.2020.0504关注微信公众号，获得更多资讯信息Journal of Agricultural Resources and Environment农 业 资 源 与 环 境 学 报 2025, 42（5）: 1125-1139http://www.aed.org.cnApplication of machine learning in identification，remediation，and risk assessment of farmland soil pollution：a reviewLI Xingzhen1, LIN Hansen1, QIU Shaojian2, LIN Qingqi1, 3, 4*, YE Long5, MAI Yuebang5, WU Peihao5, NI Zhuobiao1, 3, 4, QIU Rongliang1, 3, 4, 6（1. College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China; 2. College ofMathematics and Information, South China Agricultural University, Guangzhou 510642, China; 3. Guangdong Provincial Key Laboratory ofAgricultural and Rural Pollution Control and Environmental Safety, Guangzhou 510642, China; 4. Guangdong Laboratory for LingnanModern Agriculture, Guangzhou 510642, China; 5. Guangdong Provincial Academy of Building Research Group Co., Ltd., Guangzhou510510, China; 6. School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China）Abstract：Farmland, as a protected land resource of the state, is inextricably related to agricultural production, food safety, and humanhealth. Consequently, investigating the soil pollution status of farmland is of significant importance for ensuring food quality andsafeguarding farmland resources. Traditional research on farmland soil pollution have primarily focused on individual scenarios, specificpollutants, or singular experimental conditions, which complicates the resolution of increasingly complex environmental issues. With theadvent of the big data era, machine learning has been increasingly applied in the field of farmland soil environmental protection,demonstrating its advantages in objectivity, accuracy, and proficiency in addressing complex tasks related to soil pollution identification机器学习在农田土壤污染识别、修复和风险评估中的应用研究进展李杏桢 1，林汉森 1，邱少健 2，林庆祺 1，3，4*，叶龙 5，麦粤帮 5，吴培浩 5，倪卓彪 1，3，4，仇荣亮 1，3，4，6（1. 华南农业大学资源环境学院，广州 510642；2. 华南农业大学数学与信息学院，广州 510642；3. 广东省农业农村污染治理与环境安全重点实验室，广州 510642；4.岭南现代农业科学与技术广东省实验室，广州 510642；5.广东省建筑科学研究院集团股份有限公司，广州 510510；6.中山大学环境科学与工程学院，广州 510006）收稿日期：2024-10-07录用日期：2024-12-06作者简介：李杏桢（2000—），女，广东广州人，硕士研究生，研究方向为基于机器学习的土壤污染风险管控与评估。E-m