UNMANNED AERIAL VEHICLE (UAV) DERIVED NORMALISED DIFFERENCE VEGETATION INDEX (NDVI) AND CROWN PROJECTION AREA (CPA) TO DETECT HEALTH CONDITIONS OF YOUNG OIL PALM TREES FOR PRECISION AGRICULTURE

Abstract

Abstract. Malaysia currently is one of the biggest global producers and exporters of palm oil. The world’s expanding oil palm plantation areas contribute to climate change and in-return, climate is change also affecting the health of oil palms through a range of abiotic and biotic stresses. Current advancements in Precision Agriculture research using UAV gives an advantage to detect the health conditions of oil palm at early stages. Thus, remedial actions can be taken to prolong the life and increase oil palms productivity. This paper explores the use of UAV derived NDVI and CPA of young oil palm to detect the health conditions. NDVI of individual oil palm were extracted using ground masking layer from the dense point clouds and visual on-screen manual editing was done for removing trees other than oil palm in ENVI software. The classified individual crown NDVI were then processed to extract the mean NDVI also conversion to vector to obtain the individual crown outline. Extracted mean NDVI was classified into un-healthy and healthy trees while the CPA was classified into small, medium and big size classes. These classes of NDVI and CPA were analysed using GIS overlay method thus revealing the spatial patterns of individual oil palm trees and its health conditions. Overall, the majority of oil palm trees of the study area are healthy but average performing. However, few oil palm trees detected having health problems which has low NDVI and small CPA. This study demonstrates that biophysical parameters such as the CPA can be used to detect individual young oil palm trees health conditions and problems when combined with vegetation indices such as NDVI.