水稻叶片氮含量光谱监测中使用连续投影算法的可行性

使用5段移动平滑法、基线校正、光谱面积归一化、多元散射校正方法对水稻叶片可见-近红外光谱进行预处理，使用连续投影算法（SPA）进行有效波长的选取。分别基于光谱指数RVI、NDVI建立多元线性回归（MLR）模型，基于SPA有效波长建立MLR模型，基于全部波长建立主成分回归（PCR）及偏最小二乘法（PLS）回归模型。利用模型预测水稻叶片氮含量，对比发现基于SPA有效波长建立的模型的预测效果明显好于基于光谱指数RVI及NDVI建立的模型，略差于基于全部波长建立的PCR及PLS模型。基于MSC预处理光谱及SPA有效波长建立的模型预测集预测结果r=0.7943，RMSE=0.4558。在水稻叶片氮含量光谱监测中使用连续投影算法进行有效波长的选取是可行的。

Feasibility of using successive projections algorithm in spectral monitoring of rice leaves nitrogen contents

5 segments moving average, baseline correction, area normalization, and multiplicative scatter correction (MSC) was used to preprocess Visible-NIR reflective spectrum of rice leaf. Successive projection algorithm (SPA) was used in the selecting of effective wavelengths. Multiple linear regression (MLR) models were built based on spectral indexes of RVI, NDVI and effective wavelengths selected by SPA. Principal components regression (PCR) models and Partial least squares regression (PLS) models were built based on all wavelengths in the spectrum. Nitrogen contents of rice leaves were predicted by these models. From comparison, It was found that the predictive validity of models based on SPA effective wavelengths were obviously better than models based on spectral indexes of RVI and NDVI, and slightly worse than PCR and PLS models based on all wavelengths in the spectrum. Models based on MSC preprocessed spectrum and SPA effective wavelengths has the predictive validity of r=0.7943, RMSE=0.4558. It is feasible to use successive projections algorithm in spectral monitoring of rice leaves nitrogen contents.