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根据不同叶片层中的玉米叶的光谱识别铜和污染元素

Jianhong Zhang¹, Min Wang², Keming Yang³

¹Piesat Information Technology Co., Ltd, 2035 Laboratory, Beijing 100083, China; School of Information Engineering, China University of Geosciences Beijing, Beijing 100083, China.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

|December 9, 2024

概括

玉米叶的超光谱遥感有效地识别重金属污染. 新的光谱处理方法,如多变量实证模式分解 (MEMD),提高了检测铜 (Cu) 和 (Pb) 污染的准确性.

科学领域:

环境科学环境科学
农业科学农业科学
遥感技术遥感技术遥感技术

背景情况:

工业化加剧了土壤重金属污染,严重影响了作物生长,需要有效的监测方法.
超光谱遥感显示出使用作物叶子光谱识别重金属污染的前景,但需要先进的光谱处理技术.

研究的目的:

调查超光谱数据和先进处理方法的潜力,以区分玉米叶中的铜 (Cu) 和 (Pb) 污染.
开发和评估用于准确识别农业土壤中重金属污染的机器学习模型.

主要方法:

温室实验模拟玉米植物的Cu和Pb压力,从不同叶片层收集超光谱数据.
使用多变量实证模式分解 (MEMD),第一导数 (FD) 和第二导数 (SD) 的光谱数据预处理.
使用竞争性适应性重权取样 (CARS) 和代保留信息变量 (IRIV) 的特征频段选择.
机器学习模型 (SVM,ELM,XGBoost) 的构建,用于污染元素的歧视.

主要成果:

使用MEMD转换的光谱数据的模型在区分Cu和Pb污染方面表现出比FD和SD方法更好的性能.
在培训和验证组中,MEMD-CARS-SVM和MEMD-CARS-ELM模型实现了超过80%的准确性,用于识别Cu和Pb.
该研究证实了MEMD在高光谱数据处理和重金属识别方面的可行性和有效性.

结论:

玉米叶的高光谱分析,特别是使用MEMD预处理,为准确识别Cu和Pb污染提供了一种可靠的方法.
开发的光谱预处理和机器学习方法为大面积的土壤重金属污染监测提供了新的视角.
这项研究为在环境监测和农业管理中利用超光谱遥感提供了有价值的科学参考.

关键词:

玉米叶子玉米叶子歧视的元素是歧视的元素.重金属污染污染重金属污染超光谱遥感是一种超光谱遥感技术.

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