Predicting Apple Tree Macronutrients Using Unmanned Aerial Vehicle-Based Hyperspectral Imagery to Manage Apple Orchard Nutrients

Abstract

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 R-2 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 R-2 <= 0.37. The estimation performance for the other macronutrients was as follows: R-2 >= 0.70 and RMSE <= 1.43 mu g/cm(2) for Ch; R-2 >= 0.44 and RMSE <= 0.04% for P; R-2 >= 0.53 and RMSE <= 0.23% for K; R-2 >= 0.85 and RMSE <= 6.18% for C; and R-2 >= 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.