Using visible spectral-index as alternative methods for identifying levels of Ganoderma Boninese infection

Abstract

Ganoderma boninense is a major devastating disease for oil palm. The severity level identification of Ganoderma boninense on oil palm plantation is important to support the decision making on managerial activities. There have been researches conducted about the usage of unmanned aerial photograph (UAV) on oil palm plantation, nonetheless, the utilization of digital data on the visible aerial photograph has not optimally used. This study aims to obtain alternative methods to identify the severity level of Ganoderma boninense infection with visible spectral index from a visible aerial photograph (RGB). Visible aerial photograph (RGB-aerial photograph) is adopted on this research and carried out at Dusun Ulu plantation with various visible spectral-index methods. The visible spectral-index methods are the excess green index (ExG), the excess red index (ExR), the excess green minus excess red index, and the colour of vegetation extraction (CIVE). The results of four visible spectral-index methods are able to differentiate the severity level of Ganoderma boninense infection on each individual of oil palm.