栗方亮　孔庆波　张青

摘  要：鉀素是蜜柚营养三要素之一，是准确诊断和定量评价生长状况的重要指标，建立合适的蜜柚叶片钾素含量高光谱估算模型，为实现快速、无损、精确的钾素含量估测提供依据。基于蜜柚叶片高光谱数据和钾素含量实测数据，首先分析蜜柚叶片钾素含量与原始及一阶微分光谱的相关性，然后分析与敏感波段植被参数的相关性，并找出相关性较好的光谱参数，建立蜜柚叶片钾素含量偏最小二乘回归模型（PLS）、BP神经网络回归模型（BPNN）、随机森林回归模型（RF）和支持向量机回归模型（SVM），并确定蜜柚叶片钾素含量最佳估算模型。在513～598 nm和699～735 nm处，蜜柚叶片钾素含量与原始光谱反射率呈显著负相关，最大负相关系数分别为‒0.47（554 nm）和‒0.45（715 nm）。在507～552 nm和691～711 nm处，蜜柚叶片钾素含量与一阶微分光谱反射率呈显著负相关，最大负相关系数分别为‒0.54（528 nm）和‒0.53（702 nm）;在557～655 nm处，二者呈显著正相关，最大正相关系数为0.58（579 nm）。选择554、715、528、579、702 nm构建光谱参数，建立差值植被指数（DVI）、比值植被指数（RVI）和归一化植被指数（NDVI）等，其中与蜜柚叶片钾素含量相关性较好的光谱参量为NDVI′、RVI、RVI′、R′。建立PLS、BPNN、RF和SVM等4种回归模型估算蜜柚叶片钾素含量并进行对比验证，4种估算模型的决定系数（）分别为0.72、0.74、0.84和0.81，均方根误差（RMSE）分别为2.44、2.28、1.49和1.61;相对误差（RE）分别为9.95%、9.01%、7.84%和8.01%。验证模型的R分别为0.79、0.84、0.85和0.82，RMSE分别为1.69、1.48、1.34和1.41，RE分别为8.47%、7.70%、6.12%和6.35%，RF估算模型精度稍高于PLS、BPNN和SVM估算模型。

关键词：高光谱;蜜柚;钾素;光谱指数中图分类号：S666.3;S127      文献标识码：A

Comparative Estimation of Potassium Contents in Guanxi Honey Pomelo Leaves by Multiple Regression Models

LI Fangliang KONG QingboZHANG Qing

Institute of Soil and Fertilizer， Fujian Academy of Agricultural Sciences， Fuzhou， Fujian 350013， China

Potassium （K） is one of the three nutrient elements of honey pomelo， which is an important index for accurate diagnosis and quantitative evaluation of growth status. The hyperspectral estimation model of K contents in honey pomelo leaves was established to provide basis for rapid， non-destructive and accurate estimation of K contents. Based on the hyperspectral data of pomelo leaves and the measured data of K contents， this study first analyzed the correlation between the K contents of pomelo leaves and the original and first-order differential spectra， then analyzed the correlation between sensitive band vegetation index and the K contents of pomelo leaves， and found out the spectral parameters with good correlation with the K contents of pomelo leaves， then the partial least squares regression model （PLS）， BP neural network regression model （BPNN）， random forest regression model （RF） and support vector machine regression model （SVM） of pomelo leaves K contents were established， and the best estimation model of K contents in pomelo leaves was determined. In the measured band of 350‒1050 nm， the spectral reflectance of pomelo leaves decreased with the increase of K contents. In 513‒598 nm and 699‒735 nm， it reached a significant negative correlation level， the maximum negative correlation coefficient was ‒0.47 （554 nm） and ‒0.45 （715 nm）， respectively. In 507‒552 nm and 691‒711 nm， potassium in pomelo leaves reached a significant negative correlation level with the first-order spectral reflectance， the maximum negative correlation coefficient was ‒0.54 （528 nm） and ‒0.53 （702 nm）. In 557‒655 nm， it reached a significant positive correlation level， and the maximum positive correlation coefficient was 0.58 （579 nm）. 554， 715， 528， 579， 702 nm were selected to construct the spectral parameters and establish the difference vegetation index （DVI）， ratio vegetation index （RVI） and normalized difference vegetation index （NDVI））. NDVI¢， RVI， RVI¢， R¢were the spectral parameters that had good correlations with the K contents of pomelo leaves. Four regression models such as PLS， BPNN， RF and SVM were established to estimate K content in pomelo leaves and verified. ， RMSE and RE of the estimation model of pomelo leaves K contents established by RF method was 0.84， 1.49 and 7.84%， respectively. ， RMSE and RE of the estimation model by SVM method were 0.81， 1.61 and 8.01% respectively. While ， RMSE and RE of the estimation model by BPNN method were 0.74， 2.28 and 9.01% respectively; ， RMSE and RE of the estimation model by PLS method were 0.72， 2.44 and 9.95% respectively. of the validation model of PLS， BPNN， RF and SVM methods were 0.79， 0.84， 0.85 and 0.82 respectively. Compared with PLS， BPNN and SVM， RF had higher ， lower RMSE and lower RE， indicating that the accuracy of RF based K contents estimation model was higher than that of PLS， BPNN and SVM. Through the comparison of four hyperspectral estimation models for K contents in Guanxi honey pomelo leaves， the accuracy of random forest estimation model was higher than that of PLS， BPNN and SVM.

hyperspectral; honey pomelo; K element; spectral index

10.3969/j.issn.1000-2561.2022.06.012

琯溪蜜柚原产于福建省漳州市平和县，距今已有500多年的栽培历史，截至2016年，其栽培面积达4.33万hm，年产量120万t。……