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 (R2 ), 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-RSPLSR model exhibited the best performance of LCCtot estimation; with the number of principal components (Pcs) = 5, this model provided high values of the R2 of calibration (C-R2 ) = 0.92 and the R2 of validation (V-R2 ) = 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.

Introduction

Chlorophyll is the main photosynthetic pigment present in green plants and plays an important role in controlling carbon exchange and plant productivity [1], [2]. The chlorophyll content increases in young expanding leaves, reaches the highest value at maturity, and then decreases significantly during senescence [3], [4]. Therefore, the chlorophyll content of plant leaves correlated with the nutritional status can theoretically be used as a marker of the growth status of plants. Measurements and estimates of chlorophyll content are regarded as a meaningful indicator of plant health, including nitrogen deficiency, water stress and certain diseases [2], which can provide theoretical guidance for crop nutrient diagnosis and field management. The traditional wet-chemical method for measuring chlorophyll is precise but costly, time-consuming and inapplicable to large-scale analysis. Hence, scientists have been developing convenient and rapid methods for the measurement of leaf chlorophyll utilizing its unique optical absorption feature. Extracting chlorophyll information from the spectral features of plants has become a major means of estimating chlorophyll contents because of its advantages of being fast, nondestructive and large-scale [1], [5]–[۸]. Numerous studies have been conducted using spectral data to retrieve chlorophyll information as a function of time and space in environments such as the ground or airborne and spaceborne environments [9]–[۱۶].