Authors: Zhongzheng Peng | Lixin Guan | Yubo Liao | Suyun Lian
Source: IEEE Access (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Estimating Total Leaf Chlorophyll Content of Gannan Navel Orange Leaves Using Hyperspectral Data Based on Partial Least Squares Regression The goal of this study was to model the total leaf chlorophyll content (LCCtot) of Gannan navel orange leaves using a field imaging spectroscopy system in the visible and near-infrared domain. The spectral range from 400 to 1000 nm with 176 wavebands (a wavelength interval of 3.41 nm) or 360 wavebands (a wavelength interval of 1.67 nm), labeled as “Datasets_1.67” and “Datasets_3.41”, respectively, were used. Although different spectral data types were used, better prediction results for LCCtot were based on Datasets_1.67 for LCCtot prediction. Several prediction models of LCCtot were built based on partial least squares regression (PLSR), artificial neural networks (ANN), ordinary least squares regression (OLSR), and stepwise linear regression (SLR) using full spectral and effective wavelength (EW) data (raw spectral (RS), first derivative spectral (FDS) and second derivative spectral (SDS) data). The determination coefficient (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ), the root mean square error (RMSE) and the residual predictive deviation (RPD) were used to evaluate the reliability and accuracy of the predicted LCCtot values. As a result, 14 (7 obtained from Datasets_1.67, 7 obtained from Datasets_3.41), 39 (21 obtained from Datasets_1.67, 18 obtained from Datasets_3.41) and 50 (27 obtained from Datasets_1.67, 23 obtained from Datasets_3.41) wavebands were selected from the RS data, FDS data and SDS data, respectively, as the EWs for LCCtot prediction of navel orange leaves. After that, PLSR and ANN predictive models were established using full spectra, and OLSR and SLR predictive models were built using the selected EWs. The experimental results demonstrated that these various regression methods were useful for estimating LCCtot in the order of PLSR models established using full spectra from RS data (F-RS-PLSR) > PLSR models established using full spectra from SDS data (F-SDS-PLSR) > PLSR models established using full spectra from FDS data (F-FDS-PLSR) > SLR models established using EWs by RS data (EWs-RS-SLR). However, models built with ANN and OLSR, where the RPD values were less than 3, cause the models to be inaccurate. Finally, in comparison, the F-RS-PLSR model exhibited the best performance of LCCtot estimation; with the number of principal components (Pcs) = 5, this model provided high values of the R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of calibration (C-R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) = 0.92 and the R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of validation (V-R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) = 0.96, small values of the RMSE of calibration (C-RMSE)=0.05 mg/g and the RMSE of validation (V-RMSE) = 0.19 mg/g, and sufficient the RPD of calibration (C-RPD)=17.00 and the RPD of validation (V-RPD)=3.63 values. Overall, the best modeling method was PLSR. Hence, the PLSR applicability for assessing chlorophyll content in navel orange leaves was demonstrated. Partial least squares regression ; Mean squared error ; Hyperspectral imaging ; Mathematics ; Residual ; Coefficient of determination ; Linear regression ; Regression analysis ; Remote sensing ; Regression ; Content (measure theory) ; Statistics ; Computer science ; Algorithm ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Leaf Properties and Growth Measurement