Spectral Estimation of Chlorophyll for Non-Invasive Assessment in Apple Orchards

Abstract

The main aim of our research was to develop a methodology of chlorophyll content in the leaves of apple trees non-invasive assessment in apple orchards and its adaptation to Early Gold and Golden Reinders based on spectral characteristics of chlorophyll content in the canopy. In each measurement period, 30 samples were collected from each of the two apple cultivars studied. For spectral data collection of leaf samples, an AvaSpec 2048 spectrometer was used in the wavelength range 400–1000 nm in three replicates. Principal component analysis (PCA) with varimax rotation was used to identify the wavelength with the highest factor weight to identify the chlorophyll-sensitive wavelength. The models were calibrated with 2/3 of the values in the database and validated with the remaining 1/3. The simple linear regression method generated the model for estimating chlorophyll. The coefficient of determination (R2) was used to compare the strength of the regression models, and the Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), Nash–Sutcliffe efficiency (NSE), Mean Absolute Error (MAE) and Mean Bias Error (MBE) functions were used to measure the accuracy of the estimator models. These metrics help to quickly assess how reliable and accurate a model’s predictions are. Nine indices were obtained based on the precision values, and CHLapple1 performed best (R2 = 0.633, RMSE = 298.28 µg/g, NRMSE = 9.61%, NSE = 0.60, MBE = 84.59, and MAE = 243.39).