冬小麦苗期叶绿素含量检测光谱学参数寻优

光谱分析技术是作物生长检测的主要手段,为了解决大田漫反射采集所造成的光谱基线漂移和偏移问题,研究采集了冬小麦冠层325~1 075 nm范围反射光谱,采用多元散射校正方法对小麦原始光谱进行预处理。采取遗传算法对光谱特征参数寻优并结合相关分析结果,选取486、599、699和762 nm波长处反射率值并组合计算了RVI(ratio vegetation index),DVI(difference vegetation index),NDVI(normalized difference vegetation index)和SAVI(soil-adjusted vegetation index)共12个植被指数,分析了各植被指数与叶绿素含量值之间的相关关系,结果显示:DVI和SAVI可抑制苗期土壤背景干扰并对叶绿素含量响应较为敏感,与叶绿素含量相关性最优的参数分别为DVI(762,599)、SAVI(762,599)、DVI(762,699)和SAVI(762,699),与叶绿素含量的相关系数都达到0.6以上。基于相关性最优光谱植被指数DVI(762,699)和SAVI(762,599)利用最小二乘-支持向量回归建立冬小麦叶绿素含量预测模型,建模集决定系数为0.681,验证集决定系数为0.611。该模型可用于无损检测冬小麦苗期叶绿素含量,以期为后续施肥决策提供支持。

Optimization of spectroscopy parameters and prediction of chlorophyll content at seeding stage of winter wheat

Abstract: Accurate prediction of winter wheat chlorophyll content at seedling stage is important for guiding precision management in the field. In order to acquire chlorophyll content of winter wheat leaves, traditional detection methods require to squash winter wheat leaves and applying chemical methods, which would have bad influence on crops growth and cause unnecessary waste of time on some level. It is proved that the spectroscopy analysis is an effective method to predict chlorophyll content of winter wheat. However, the drift and offset of spectral baseline has a great influence on the predicting accuracy. So, this study was carried out to eliminate the influence of the drift and offset. The experimental farm was randomly divided into 70 different sampling areas in Xiaotangshan, Beijing, and the winter wheat leaves were collected on April 20th in the period of seedling stage. The visible and near infrared canopy spectral reflectance of winter wheat was measured by an ASD FieldSpec handheld spectroradiometer at seedling stage. The chlorophyll contents of sampling leaves were detected by the spectrophotometer in the laboratory on the same day. The obtained data of the canopy spectral reflectance and chlorophyll content were assembled for each region individually. The multiple scattering correction (MSC) was used on the bands of 325-1025 nm wavelength, because many scattering errors were introduced into the measured spectral data due to the physical factors. The MSC method first requires establishing an ideal spectrum of all samples, and modifying all the other samples of near infrared spectra on the basis of ideal spectrum to, and spectral reflectance changes with the content of chlorophyll components in the sample meet the direct linear relationship. The absolute intensity difference of spectral reflectance of winter wheat canopy was weakened after the MSC pretreatment, and then scattering effect was effectively reduced. Baseline shift and offset problems were resolved, and the correlation coefficients of spectral reflectance and chlorophyll content were increasing through the MSC pretreatment. Furthermore, genetic algorithm (GA) was proposed for sensitive band selection. GA is a high efficient and globally random search optimization method which simulates Darwin''s evolution by natural selection and genetic mechanism of biological evolution. According to the principles of choosing high frequency bands as characteristic wavelength, 486, 599, 699 and 762 nm crop canopy reflectances were selected to calculate vegetation indices, including ratio vegetation index (RVI), difference vegetation index (DVI), normalized difference vegetation index (NDVI) and soil-adjusted vegetation index (SAVI). The correlation between each vegetation index and chlorophyll content of winter wheat was analyzed. It was found that the correlation between each vegetation index and chlorophyll content of winter wheat significantly increased after the MSC. The results showed that DVI and SAVI could refrain interference of soil background during seedling period, and the optimal parameters were DVI(762, 599), SAVI(762, 599), DVI(762, 699) and SAVI(762, 699), and the correlation coefficients were all above 0.6. The DVI(762, 699) and SAVI(762, 599) were selected to establish the multiple linear regression (MLR) prediction model and the least squares-support vector regression (LS-SVR) prediction model. The 70 winter wheat samples were divided into 2 groups, 50 samples for model calibration and the other 20 samples for model verification. The results of MLR showed the determination coefficient of the calibration model was 0.528 and that of the validation model was 0.487. In order to improve the precision of the forecast model, the LS-SVR prediction model was applied, and the determination coefficient of the calibration model was 0.681 and that of the validation model was 0.611. It showed that the fitting result was ideal. With the application of spectral technology, it provides a feasible method to detect the winter wheat growth status at seedling stage.