PreciPalm: An Intelligent System for Calculating Macronutrient Status and Fertilizer Recommendations for Oil Palm on Mineral Soils Based on a Precision Agriculture Approach

Abstract

The measurement of the macronutrient values of an oil palm plantation is a complex and tedious task, particularly when dealing with large plantation areas. This situation complicates the process of the conventional measurement of nutrients by taking samples of oil palm leaves in the area being observed, causing delays in fertilizer recommendation and a lack of spatial diversity observation. Precision agriculture (PA) principles and approaches, which focus on assessing temporal and spatial variability, can be used to improve conventional measurement methods in terms of both accuracy and speed. This research aims to determine macronutrients, specifically nitrogen (N), phosphorus (P), and potassium (K) contents in oil palm leaves based on PA principles using the integration of remote sensing technology and machine learning to quickly obtain the macronutrient status from oil palm plantation areas. The Sentinel-1A and Sentinel-2A imagery data were analyzed and extracted to produce selected features, which are most influencing in the correlation between the imagery data and the leaf macronutrient values obtained from laboratory analysis. The random forest regression (RFR) model is used to produce correlation functions to compute macronutrient values. The use of the two satellites is to cope with cloud and smoke interference. The prototype system developed, named PreciPalm (Precision Agriculture Platform for Oil Palm), has been validated and implemented based on 2000 leaf sampling units representing several oil palm plantation areas in Indonesia, including Java, Sumatra, and Kalimantan. The observed system performance resulted in the measurement accuracy of 95.02%, 93.50%, and 82.52% for the nutrients N, P, and K, respectively. The novelty of PreciPalm is that it provides an ecosystem to transparently measure and observe the macronutrient status of oil palm in a timely, visual, spatial, and location-specific manner, thereby improving oil palm nutritional management with more certainty and precision.