中国农业大学学报2026，Vol.31Issue(6)：140-153,14.DOI:10.11841/j.issn.1007-4333.2026.06.12
基于高光谱结合机器学习方法对谷子株高的遥感监测
Remote monitoring of foxtail millet plant height using hyperspectral data and machine learning
摘要
Abstract
To develope high-accuracy models for foxtail millet plant height remote monitoring,this study used foxtail millet variety'Jingu 21 hao'as the test material.Canopy hyperspectral data were collected with an ASD FieldSpec spectrometer,and corresponding plant height measurements were taken simultaneously.Five spectral preprocessing methods-including first derivative,second derivative,standard normal variate,multiplicative scatter correction,and Savitzky-Golay smoothing-were applied to the original reflectance(R)data.This study then combined vegetation indices,characteristic wavelengths,and full-band data with four machine learning algorithms to explore their correlations with plant height and construct a monitoring model.The results showed that the optimal vegetation index(NPQI)correlated with plant height at a coefficient of-0.718.Among the four machine learning models developed,the 2ST-PLS model(R2=0.850,RMSE=6.655 cm,RPD=2.187),based on full-spectrum data across all growth stages,and the PLS model(R2=0.840,RMSE=6.102 cm,RPD=2.385)exhibited strong predictive capability.This study demonstrates the feasibility of using spectral techniques for monitoring foxtail millet plant height and provides a methodological basis for its precise,rapid,and non-destructive estimation.关键词
谷子/高光谱/株高/机器学习方法/特征波长/全波段Key words
foxtail millet/hyperspectra/plant height/machine learning methods/characteristic wavelengths/full-band分类
农业科技引用本文复制引用
常博,王海岗,王君杰,李蕊,赵世珂,张谊婷,代春阳,崔秀妍,田翔,陈凌,乔治军..基于高光谱结合机器学习方法对谷子株高的遥感监测[J].中国农业大学学报,2026,31(6):140-153,14.基金项目
农业农村部政府购买服务项目(22250587) （22250587）
山西省基础研究计划(202203021222144) （202203021222144）
