CAJ | 학술논문

以油菜叶片为研究对象，利用高光谱成像技术，成功建立了叶绿素相对值SPAD值的预测模型。共采集了160个油菜叶片样本在380～1030 nm范围内的高光谱图像。选择500～900 nm之间的平均光谱作为油菜叶片样本的光谱。利用蒙特卡罗最小二乘法（monte carlo partial least squares，MC-PLS）剔除了13个异常样本，基于剩余的147个样本光谱数据与SPAD 测量值进行分析，采用了不同的方法建立了多种预测模型，包括：全光谱的偏最小二乘法（partial least squares，PLS）模型，连续投影算法（successive projections al-gorithm，SPA）选择特征波长的PLS预测模型，“红边”位置（λred ）的简单经验估测模型，三种植被指数R710／R760，（R750－R705）／（R750－R705）和R860／（R550*R708）分别建立的简单经验估测模型，以及基于这三种植被指数的PLS预测模型。建模结果显示，全光谱的PLS模型预测效果最为精确，其预测相关系数rp 为0．8339，预测均方根误差RMSEP为1．52。而使用SPA算法选出的8个特征波长所建立的PLS模型其预测结果可达到与全光谱的PLS模型非常接近的水平，而且在保证一定精度的条件下减少了大量运算，节省了运算时间，大幅提高了建模的速度。而基于红边位置和选择的三种植被指数而建立的简单经验估计模型其预测结果虽与基于全光谱的PLS预测模型有一定差距，但模型简单、运算量小，适合用于对精度要求不高的场合，对后续的便携仪器设备开发有一定的指导作用。
이유채협편위연구대상，이용고광보성상기술，성공건립료협록소상대치SPAD치적예측모형。공채집료160개유채협편양본재380～1030 nm범위내적고광보도상。선택500～900 nm지간적평균광보작위유채협편양본적광보。이용몽특잡라최소이승법（monte carlo partial least squares，MC-PLS）척제료13개이상양본，기우잉여적147개양본광보수거여SPAD 측량치진행분석，채용료불동적방법건립료다충예측모형，포괄：전광보적편최소이승법（partial least squares，PLS）모형，련속투영산법（successive projections al-gorithm，SPA）선택특정파장적PLS예측모형，“홍변”위치（λred ）적간단경험고측모형，삼충식피지수R710／R760，（R750－R705）／（R750－R705）화R860／（R550*R708）분별건립적간단경험고측모형，이급기우저삼충식피지수적PLS예측모형。건모결과현시，전광보적PLS모형예측효과최위정학，기예측상관계수rp 위0．8339，예측균방근오차RMSEP위1．52。이사용SPA산법선출적8개특정파장소건립적PLS모형기예측결과가체도여전광보적PLS모형비상접근적수평，이차재보증일정정도적조건하감소료대량운산，절성료운산시간，대폭제고료건모적속도。이기우홍변위치화선택적삼충식피지수이건립적간단경험고계모형기예측결과수여기우전광보적PLS예측모형유일정차거，단모형간단、운산량소，괄합용우대정도요구불고적장합，대후속적편휴의기설비개발유일정적지도작용。
In the present work,prediction models of SPAD value (Soil and Plant Analyzer Development,often used as a parame-ter to indicate chlorophyll content)in oilseed rape leaves were successfully built using hyperspectral imaging technique.The hy-perspectral images of 160 oilseed rape leaf samples in the spectral range of 380~1030 nm were acquired.Average spectrum was extracted from the region of interest(ROI)of each sample.We chose spectral data in the spectral range of 500~900 nm for analy-sis.Using Monte Carlo partial least squares(MC-PLS)algorithm,13 samples were identified as outliers and eliminated.Based on the spectral information and measured SPAD values of the rest 147 samples,several estimation models have been built based on different parameters using different algorithms for comparison,including:(1 )a SPAD value estimation model based on par-tial least squares(PLS)in the whole wavelength region of 500~900 nm;(2)a SPAD value estimation model based on successive projections algorithmcombined with PLS(SPA-PLS);(3)4 kind of simple experience SPAD value estimation models in which red edge position was used as an argument;(4)4 kind of simple experience SPAD value estimation models in which three vegeta-tion indexes R710/R760 ,(R750-R705 )/(R750-R705 )and R860/(R550 ×R708 ),which all have been proved to have a good relevance with chlorophyll content,were used as an argument respectively;(5 )a SPAD value estimation model based on PLS using the 3 vegetation indexes mentioned above.The results indicate that the optimal prediction performance is achieved by PLS model in the whole wavelength region of 500~900 nm,which has a correlation coefficient(rp )of 0. 833 9 and a root mean squares error of predicted(RMSEP)of 1. 52.The SPA-PLS model can provide avery close prediction result while the calibration computation has been significantly reduced and the calibration speed has been accelerated sharply.For simple experience models based on red edge parameters and vegetation indexes,although there is a slight gap between theprediction performance and that of the PLS model in the whole wavelength region of 500~900 nm,they also have their own unique advantages which should be thought highly of:these models are much simpler and thus the calibration computation is reduced significantly,they can perform an important func-tion under circumstances in which increasing modeling speed and reducing calibration computation operand are more important than improving the prediction accuracy,such as the development of portable devices.