干旱胁迫下玉米叶片叶绿素含量与含水量高光谱成像反演方法

A Hyperspectral Image-Based Method for Estimating Water and Chlorophyll Contents in Maize Leaves under Drought Stress

[目的/意义]为实现玉米的干旱胁迫等作物生长状态的无损监测,本研究探索一种基于高光谱技术的干旱胁迫下玉米叶片叶绿素含量与含水量无损检测方法。[方法]首先使用高光谱相机采集不同干旱胁迫程度的苗期玉米叶片图像,并使用图像处理技术提取叶肉部分平均光谱。通过系统性地分析不同特征波长提取方法、机器学习回归模型对叶绿素含量和含水量预测性能的影响,分别建立最优叶绿素含量和含水量反演模型;并探究构建可用于叶绿素含量和含水量反演的植被系数并评估其反演能力。[结果和讨论]结合逐步回归(Stepwise Regression,SR)特征提取与Stacking回归可获得最优叶绿素含量预测效果(R~2为0.878,均方根误差为0.317 mg/g);结合连续投影算法(Successive Projections Algorithm, SPA)特征提取与Stacking回归可获得最优含水量预测效果(R~2为0.859,RMSE为3.75%);新构建的归一化差分植被指数[(R_(410)-R_(559))/(R_(410)+R_(559))]和比值系数(R_(400)/R_(1171))分别对叶绿素含量和含水量反演精度最高且显著高于传统植被系数,R~2分别为0.803和0.827,均方根误差分别为0.403 mg/g和3.28%。[结论]本研究构建的基于高光谱信息的反演模型与植被系数可以实现玉米叶片叶绿素含量与含水量的精确、无损检测,可为玉米生长状态实时监测提供理论依据和技术支持。

[Objectives]Chlorophyll content and water content are key physiological indicators of crop growth,and their non-destructive detec‐tion is a key technology to realize the monitoring of crop growth status such as drought stress.This study took maize as an object to develop a hyperspectral-based approach for the rapid and non-destructive acquisition of the leaf chlorophyll content and water content for drought stress assessment.[Methods]Drought treatment experiments were carried out in a greenhouse of the College of Agriculture,Guangxi University.Maize plants were subjected to drought stress treatment at the seedling stage(four leaves).Four drought treatments were set up for normal water treatment[CK],mild drought[W1],moderate drought[W2],and severe drought[W3],respectively.Leaf samples were collect‐ed at the 3rd,6th,and 9th days after drought treatments,and 288 leaf samples were collected in total,with the corresponding chlorophyll content and water content measured in a standard laboratory protocol.A pair of push-broom hyperspectral cameras were used to col‐lect images of the 288 seedling maize leaf samples,and image processing techniques were used to extract the mean spectra of the leaf lamina part.The algorithm flow framework of"pre-processing-feature extraction-machine learning inversion"was adopted for pro‐cessing the extracted spectral data.The effects of different pre-processing methods,feature wavelength extraction methods and ma‐chine learning regression models were analyzed systematically on the prediction performance of chlorophyll content and water con‐tent,respectively.Accordingly,the optimal chlorophyll content and water content inversion models were constructed.Firstly,70%of the spectral data was randomly sampled and used as the training dataset for training the inversion model,whereas the remaining 30%was used as the testing dataset to evaluate the performance of the inversion model.Subsequently,the effects of different spectral pre-processing methods on the prediction performance of chlorophyll content and water content were compared.Different feature wave‐lengths were extracted from the optimal pre-processed spectra using different algorithms,then their capabilities in preserve the infor‐mation useful for the inversion of leaf chlorophyll content and water content were compared.Finally,the performances of different machine learning regression model were compared,and the optimal inversion model was constructed and used to visualize the chloro‐phyll content and water content.Additionally,the construction of vegetation coefficients were explored for the inversion of chloro‐phyll content and water content and evaluated their inversion ability.The performance evaluation indexes used include determination coefficient and root mean squared error(RMSE).[Results and Discussions]With the aggravation of stress,the reflectivity of leaves in the wavelength range of 400~1700 nm gradual‐ly increased with the degree of drought stress.For the inversion of leaf chlorophyll content and water content,combining stepwise re‐gression(SR)feature extraction with Stacking regression could obtain an optimal performance for chlorophyll content prediction,with an R2 of 0.878 and an RMSE of 0.317 mg/g.Compared with the full-band stacking model,SR-Stacking not only improved R2 by 2.9%,reduced RMSE by 0.0356mg/g,but also reduced the number of model input variables from 1301 to 9.Combining the successive pro‐jection algorithm(SPA)feature extraction with Stacking regression could obtain the optimal performance for water content prediction,with an R2 of 0.859 and RMSE of 3.75%.Compared with the full-band stacking model,SPA-Stacking not only increased R2 by 0.2%,reduced RMSE by 0.03%,but also reduced the number of model input variables from 1301 to 16.As the newly constructed vegetation coefficients,normalized difference vegetation index(NDVI)[(R_(410)-R_(559))/(R_(410)+R_(559))]and ratio index(RI)(R400/R1171)had the highest ac‐curacy and were significantly higher than the traditional vegetation coefficients for chlorophyll content and water content inversion,re‐spectively.Their R2 were 0.803 and 0.827,and their RMSE were 0.403 mg/g and 3.28%,respectively.The chlorophyll content and wa‐ter content of leaves were visualized.The results showed that the physiological parameters of leaves could be visualized and the differ‐ences of physiological parameters in different regions of the same leaves can be found more intuitively and in detail.[Conclusions]The inversion models and vegetation indices constructed based on hyperspectral information can achieve accurate and non-destructive measurement of chlorophyll content and water content in maize leaves.This study can provide a theoretical basis and technical support for real-time monitoring of corn growth status.Through the leaf spectral information,according to the optimal mod‐el,the water content and chlorophyll content of each pixel of the hyperspectral image can be predicted,and the distribution of water content and chlorophyll content can be intuitively displayed by color.Because the field environment is more complex,transfer learn‐ing will be carried out in future work to improve its generalization ability in different environments subsequently and strive to develop an online monitoring system for field drought and nutrient stress.