The Use of the Normalized Difference Red Edge (NDRE) Vegetation Index from Multispectral Cameras Mounted on Unmanned Aerial Vehicle to Estimate the Nutrient Content in Oil Palm Leaves

Abstract

This study aimed to develop a prediction model for the nutrient content of N, P, K, Mg, and Ca in oil palm leaves using the Normalized Difference RedEdge (NDRE) vegetation index derived from multispectral camera data. Data acquisition was carried out by an unmanned aerial vehicle (UAV), which was correlated to leaf sample analysis of the 17th frond number. Results showed that simple regression analysis successfully represented nutrient content (N, P, K, Mg, and Ca) based on NDRE values. Based on the MAPE and Correctness values, the nutrient content prediction model for N and P yields reliable results, while for K, Mg, and Ca, they are considered good, with Correctness values of 95.5%, 96.6%, 88.8%, 87.3%, and 90.0% for N, P, K, Mg, and Ca, respectively. The study found that the NDRE vegetation index can be used to predict the nutrient content of oil palm leaves with reliable results in accuracy for N and P, and good accuracy for K, Mg, and Ca. This is a promising finding, as it could lead to the development of a non-destructive and rapid method for monitoring the nutrient status of oil palm trees, with the validation models for N, P, K, Mg, and Ca are yN = 1.1089x - 0.2497, yP = 0.99x + 0.002, yK = 1.204x - 0.1576, yMg = 0.9149x + 0.0183, and yCa = 1.0418x - 0.0218. Keywords: Multispectral Cameras, Oil Palm, Leaf Nutrient Contents, Prediction.