果树学报2026，Vol.43Issue(3)：673-685,13.DOI:10.13925/j.cnki.gsxb.20250384
基于无人机载高光谱成像结合机器学习算法的新疆野苹果冠层叶绿素含量估算
Estimation of chlorophyll content in the canopy of Malus sieversii based on unmanned aerial vehicle hyperspectral imaging combined with ma-chine learning algorithms
摘要
Abstract
[Objective]Malus sieversii is a significant wild fruit tree resource in the arid regions of Cen-tral Asia and the ancestral species of modern cultivated apples.The leaf chlorophyll content of M.siev-ersii normally serves as a key indicator reflecting its photosynthetic capacity and overall health status.To meet the demands of nutritional status and diseases monitoring of M.sieversii,this study established a non-destructive and rapid monitoring method for chlorophyll content in the canopy of M.sieversii us-ing a high-spectrum unmanned aerial vehicle(UAV)coupled with machine learning algorithms based on integrated spectral preprocessing techniques.The optimal machine learning model was selected to in-vert the SPAD values in the research area.[Methods]The experimental area selected the Saha Wild Fruit Forest in Xinyuan County,Hi River Valley,Xinjiang,which has typical distribution characteris-tics.During the flowering period of wild apples,the DJI M350RTK UAV was equipped with the Ga-iaSky-mini3-VN hyperspectral imaging system to obtain hyperspectral images of the canopy in the study area.Meanwhile,the relative chlorophyll contents of 85 sample tree canopies were synchronously measured using the SPAD-502Plus chlorophyll meter.During data processing,the first part of the bands with large noise(399.08-409.27)was removed.Subsequently,spectral optimization were then per-formed using eight preprocessing schemes,including four basic preprocessing methods(Savitzky-Go-lay smoothing(SG),multiplicative scatter correction(MSC),standard normal variate transformation(SNV),and moving average smoothing(MA))and their combinations with first derivative transforma-tion(D1).On this basis,three types of hyperspectral narrow-band vegetation indices were systematical-ly constructed:difference spectral index(DSI),ratio spectral index(RSI),and normalized difference spectral index(NDSI).Four algorithms,namely random forest regression(RFR),XGBoost,gradient boosting decision tree(GBDT),and K-nearest neighbor regression(KNN),were used to establish chlo-rophyll content inversion models,and the coefficient of determination(R2)and root mean square error(RMSE)were used as evaluation indicators.[Results]Spectral analysis showed that the canopy spec-trum of wild apples exhibited a reflection peak near 550 nm and a distinct chlorophyll absorption valley at 680 nm,with a steep increase in reflectance in the red edge region(700-750 nm).Without the basic preprocessing step of derivative transformation,Random Forest Regression(RFR)standed out as the best-performing preprocessing model,with an average test set R2 of 0.746(RMSE=2.707).Its advantag-es were particularly pronounced in scatter correction preprocessing,where the R2 values of MSC-RFR and SNV-RFR attained 0.772 and 0.775,respectively.The SG-D1 preprocessing method significantly re-duced the influence of high-frequency noise on the spectrum without damaging the effective spectral in-formation.Among them,MA-D1-NDSI(580.43,447.92)exhibited a correlation coefficient of 0.75(P＜0.01)with wild apple SPAD values,significantly outperforming other preprocessing methods.The char-acteristic bands were predominantly distributed in the red edge region(680-750 nm)and the near-infra-red plateau region(760-900 nm).Comparative results of the four machine learning models indicated that the RFR model exhibits the best predictive performance.Particularly under the SG-D1 preprocess-ing condition,this model achieved the R2 value of 0.818 with the RMSE of 2.419,representing a 5.5％improvement in accuracy compared to the best model of basic preprocessing(SNV-RFR).In contrast,the predictive accuracy of the XGBoost(R2=0.725),KNN(R2=0.729),and GBDT(R2=0.645)models de-creased in sequence,among which the GBDT model exhibited significant overfitting.Based on the opti-mal SG-D1-RFR model,this study generated a spatial distribution maps of the SPAD values of the M.sieversii canopy in the study area.The inversion results showed that the SPAD value of the M.sieversii canopy in the study area ranged from 17.1 to 39.8,exhibiting a distribution pattern that is higher in the southeast and lower in the northwest.Furthermore,the model could effectively distinguish plants with different health status:The SPAD values for healthy canopies clustered between 30 and 36,while those of stressed or senescent canopies predominantly exhibited values below 25.Moreover,the model suc-cessfully identified shadow areas(SPAD＞35)and dead branch areas(SPAD＜18)within the trees,vali-dating its applicability under complex canopy conditions.[Conclusion]The SG-D1 preprocessing method significantly enhanced the red-edge features and improved the correlation between spectral in-formation and chlorophyll content.The RFR algorithm performed outstanding in handling high-dimen-sional spectral features,making it the optimal choice for chlorophyll inversion of M.sieversii leaves.The constructed NDSI,RSI,and DSI indices can effectively capture the variations in chlorophyll con-tent.This method provides a novel technique for non-destructive monitoring of wild fruit tree resources,and its technical route can also be extended to the remote sensing monitoring of physiological parame-ters of other woody plants.By systematically investigating the influence of eight spectral preprocessing methods on the inversion of chlorophyll content in the canopy of M.sieversii,we proposed the optimal model combination of SG-D1-RFR and established a spectral index system suitable for complex canopy structures.Furthermore,this methodology can be applied to the monitoring and conservation of other endangered wild fruit tree resources.These studies further refine the theoretical framework and techni-cal methods for remote sensing monitoring of wild plant resources.关键词
新疆野苹果/机器学习/冠层叶绿素/无人机高光谱/植被指数Key words
Malus siersii/Machine learning/Canopy chlorophyll/UAV hyperspectral/Vegetation index分类
农业科技引用本文复制引用
张志从,崔东,赵阳,韩亚鑫,吴昀昊,江智诚,刘文新,闫江超..基于无人机载高光谱成像结合机器学习算法的新疆野苹果冠层叶绿素含量估算[J].果树学报,2026,43(3):673-685,13.基金项目
伊犁哈萨克自治州重点研究与技术开发专项(YZD2024A04) （YZD2024A04）
伊犁师范大学提升学科综合实力专项自科重点项目(22XKZZ01) （22XKZZ01）
