基于可见-近红外光谱的茄子叶绿素荧光参数Fv/Fm预测方法

Prediction of Eggplant Leaf Fv/Fm Based on Vis-NIR Spectroscopy

叶绿素荧光参数Fv/Fm是探究逆境胁迫对植物光合作用影响的重要指标,已有研究表明植被指数与Fv/Fm线性相关,但直接将植被指数与Fv/Fm拟合存在精度不足的问题。为实现对该参数的准确预测,本文以茄子为研究对象,提出一种基于可见-近红外光谱的Fv/Fm预测方法。试验获取不同生长状态茄子叶片的可见-近红外光谱数据和荧光参数,使用蒙特卡洛采样法(MCS)去除明显异常样本,采取3种光谱预处理方法及5种特征波长选择算法进行光谱数据处理,并建立偏最小二乘回归(PLSR)模型进行方法评估。基于提取出的最优特征波长组合,分析误差反传(BP)神经网络、径向基函数(RBF)神经网络、极限学习机(ELM)及回归型支持向量机(SVR)共4种机器学习算法对Fv/Fm预测模型精度的影响,从而确定基于最优方法组合的叶绿素荧光参数Fv/Fm预测方法。结果表明:茄子叶片光谱反射率随Fv/Fm的增加呈明显下降趋势,表明利用光谱信息反演Fv/Fm的可行性。基于393组试验样本,使用多元散射校正(MSC)、标准正态变量变换(SNV)进行光谱预处理,以竞争性自适应重加权采样法结合连续投影法(CARS+SPA)进行特征波长筛选的效果最优。其中, MSC-CARS-SPA-PLSR和SNV-CARS-SPA-PLSR的测试集决定系数分别为0.896 1和0.881 2,均方根误差为0.011 8和0.012 6,两者精度皆高于全光谱数据对应的PLSR模型;同时,两方法提出的特征波长个数均为12个,仅占全光谱波长个数(1 358)的0.88%。该结果表明以上两种方法有效提取出了对模型预测有利的少量波长。基于上述波长建立机器学习模型,发现SVR建模效果最优。以SNV-CARS-SPA-SVR的预测精度最高,其测试集决定系数为0.911 7,均方根误差为0.010 8。综上, SNV-CARS-SPA-SVR建模方法提高了模型精度,有效降低了模型复杂度,为基于可见-近红外光谱的Fv/Fm准确预测提供了实现方法。该方法可应用于作物生长状态的快速、无损检测,为农情预警提供有效手段。

Chlorophyll fluorescence parameter Fv/Fm is an important indicator to investigate the effects of stress on plant photosynthesis. Previous studies showed a high linear correlation between vegetation index and Fv/Fm. However, fitting Fv/Fm and vegetation index directly showed insufficient an accuracy. In order to achieve accurate prediction of this parameter, this research took eggplant as the research object, and proposed a Fv/Fm prediction method based on Vis-NIR Spectroscopy. The experiment obtained visible-near infrared spectrum data and Fv/Fm of eggplant leaves in different growth states, Monte Carlo Sampling(MCS) method was used to remove obvious abnormal samples. Three spectral preprocessing methods and 5 characteristic wavelength selection algorithms were adopted for spectral data processing. Partial least squares regression(PLSR) models were built to evaluate these methods. Based on the optimal characteristic wavelength combinations, Fv/Fm prediction models were established by four machine learning algorithms: back propagation(BP) neural network, radial basis function(RBF) neural network, extreme learning machine(ELM), and regression support vector machine(SVR). The effects of the algorithms on the accuracy of the Fv/Fm prediction model were analyzed. Therefore, the optimal combination of the above methods, for Fv/Fm prediction was confirmed. The results were as follows: the spectral reflectance of eggplant leaves decreased significantly with the increase of Fv/Fm, indicating the feasibility of retrieving Fv/Fm by spectral information. Based on 293 sets of experimental samples, two sets of characteristic wavelengths with optimal modeling effect were extracted, which were pre-processed by multivariate scattering correction(MSC) and standard normal variable transformation(SNV) respectively, and screened by the combination use of competitive adaptive reweighted sampling method and successive projections algorithm(CARS+SPA). Among them, the test set determination coefficient(R^2) of MSC-CARS-SPA-PLSR and SNV-CARS-SPA-PLSR was 0.896 1 and 0.881 2 respectively. The root means square error was 0.011 8 and 0.012 6. Both showed higher accuracy than the PLSR model of the full spectrum data. Meanwhile, both methods selected 12 characteristic wavelengths, which only accounted for 0. 88% of the full spectrum(1 358). This indicated a small number of wavelengths conducive to model accuracy were selected. Among the machine learning models established by optimal wavelengths, SNV-CARS-SPA-SVR obtained the highest prediction accuracy, with a determination coefficient of 0.911 7 and root mean square error of 0.010 8 the test set. Thus, the SNV-CARS-SPA-SVR modeling method used in this research improved the accuracy of the model and effectively reduced the complexity of the model, providing an implementation method for accurate prediction of Fv/Fm based on the visible-near infrared spectrum. This method can be further applied in rapid and non-destructive detection of crop growth status and early warning of agricultural conditions.