基于RF-VR的紫丁香叶片叶绿素含量高光谱反演

利用高光谱技术精确估测植物叶片叶绿素含量,对植物生长趋势和营养状况的监测和管理具有重要意义。本文以紫丁香为研究对象,针对高光谱所含波段数量大、波段间相关性强导致数据中冗余信息增多的现象,通过卷积平滑和二阶微分(SG-SD)处理光谱数据,应用随机蛙跳(RF)算法筛选特征波段,最后结合偏最小二乘(PLSR)和投票回归器(VR)建立了植物叶片叶绿素含量反演模型,并与全波段光谱法和5种经典变量提取方法进行了比较。结果显示,相比于原始光谱数据,SG-SD是一种有效的提高建模精度的光谱预处理方法;相比于全波段光谱和经典变量提取方法,RF算法筛选出的敏感波段建模效果最佳;相比于PLSR模型,VR模型的预测精度和预测稳定性能更优。本文对原始光谱数据进行SG-SD预处理后,对经RF算法筛选出的特征波段建立VR模型,变量数由全波段数204个减少为35个,建模集决定系数0.944 2,验证集决定系数0.951 4,最后利用RF-VR模型结合伪彩图技术得到紫丁香叶片叶绿素分布反演图,为紫丁香叶片养分分布提供更直观的信息表达。结果表明,该方法可为紫丁香叶片营养含量诊断和长势监测提供技术支持。

Hyperspectral retrieval for chlorophyll contents of Syringa oblata leaves based on RF-VR

Accurate estimation of the chlorophyll content of plant leaves by using hyperspectral technology is of great significance to monitor and manage plant growth trends and nutritional status. Taking Syringa oblata as the research object, aiming at the phenomenon that the large number of bands and strong correlation between bands lead to the increase of redundant information in the data, the spectral data were processed by convolution smoothing and second-order differentiation (SG-SD), the random leapfrog (RF) algorithm was used to screen the characteristic bands, and finally combined with partial least squares (PLSR) and voting regression (VR). The inversion model of chlorophyll content in plant leaves was established and compared with full band spectroscopy and five classical variable extraction methods. The results showed that compared with the original spectral data, SG-SD was an effective spectral pretreatment method to improve modeling accuracy; compared with full-band spectrum and 5 classical variable selection methods, the sensitive bands selected by the RF algorithm had the best modeling accuracy; compared with PLSR model, the prediction accuracy and stability of the VR model were better. In the present paper, after the SG-SD pretreatment of the original spectral data, a VR model was established for the sensitive bands selected by the RF algorithm, the variable number reduced from 204 to 35, the determination coefficients of modeling set and validation set were 0.944 2 and 0.951 4 respectively. Finally, using RF-VR model and pseudo color map technology, the inversion map of chlorophyll distribution of Syringa oblata leaves was obtained, which provided more intuitive information expression for nutrient distribution of Syringa oblata leaves. It was concluded that this method could provide technical support for the diagnosis and growth monitoring of the nutrient content of Syringa oblata leaves.