Unmanned aerial vehicle-based multispectral imagery including five spectral bands (blue, green, red, red-edge, and near-infrared) for a rice field in the ripening stage was used to develop regression models for predicting the rice yield and protein content and to select the most suitable regression analysis method for the year-invariant model: partial least squares regression, ridge regression, and artificial neural network (ANN). The regression models developed with six vegetation indices (green normalization difference vegetation index (GNDVI), normalization difference red-edge index (NDRE), chlorophyll index red edge (CIrededge), difference NIR/Green green difference vegetation index (GDVI), green-red NDVI (GRNDVI), and medium resolution imaging spectrometer terrestrial chlorophyll index (MTCI)), calculated from the spectral bands, were applied to single years (2018, 2019, and 2020) and multiple years (2018 + 2019, 2018 + 2020, 2019 + 2020, and all years). The regression models were cross-validated through mutual prediction against the vegetation indices in nonoverlapping years, and the prediction errors were evaluated via root mean squared error of prediction (RMSEP). The ANN model was reproducible, with low and sustained prediction errors of 24.2 kg/1000 m² ≤ RMSEP ≤ 59.1 kg/1000 m² in rice yield and 0.14% ≤ RMSEP ≤ 0.28% in rice-protein content in all single-year and multiple-year analyses. When the importance of each vegetation index of the regression models was evaluated, only the ANN model showed the same ranking in the vegetation index of the first (MTCI in both rice yield and protein content) and second importance (CIrededge in rice yield and GRNDVI in rice-protein content). Overall, this means that the ANN model has the highest potential for developing a year-invariant model with stable RMSEP and consistent variable ranking.

The total nonstructural carbohydrate (TNC) concentration is an important indicator of the growth period and health of fruit trees. Remote sensing can be applied to monitor the TNC concentration in crops in a non-destructive manner. In this study, hyperspectral imaging from an unmanned aerial vehicle was applied to estimate the TNC concentration in apple trees. Partial least-squares regression, ridge regression, and Gaussian process regression (GP) were used to develop estimation models, and their effectiveness using selected key bands as opposed to full bands was evaluated in an effort to reduce computational costs and improve reproducibility. Nine key bands were identified, and the GP-based model using these key bands performed almost as well as the models using full bands. These results can be combined with previous studies on estimating the nitrogen concentration to provide useful information for more precise nutrient management to improve the yield and quality of apple trees.

Herein, the development of an estimation model to measure the chlorophyll (Ch) and macronutrients, such as the total nitrogen (T-N), phosphorus (P), potassium (K), carbon (C), calcium (Ca), and magnesium (Mg), in apples is detailed, using key band ratios selected from hyperspectral imagery acquired with an unmanned aerial vehicle, for the management of nutrients in an apple orchard. The k-nearest neighbors regression (KNR) model for Ch and all macronutrients was chosen as the best model through a comparison of calibration and validation R² values. As a result of model development, a total of 13 band ratios (425/429, 682/686, 710/714, 714/718, 718/722, 750/754, 754/758, 758/762, 762/766, 894/898, 898/902, 906/911, and 963/967) were selected for Ch and all macronutrients. The estimation potential for the T-N and Mg concentrations was low, with an R² ≤ 0.37. The estimation performance for the other macronutrients was as follows: R² ≥ 0.70 and RMSE ≤ 1.43 μg/cm² for Ch; R² ≥ 0.44 and RMSE ≤ 0.04% for P; R² ≥ 0.53 and RMSE ≤ 0.23% for K; R² ≥ 0.85 and RMSE ≤ 6.18% for C; and R² ≥ 0.42 and RMSE ≤ 0.25% for Ca. Through establishing a fertilization strategy using the macronutrients estimated through hyperspectral imagery and measured soil chemical properties, this study presents a nutrient management decision-making method for apple orchards.