Classifying the severity of basal stem rot disease in oil palm plantations using WorldView-3 imagery and machine learning algorithms

Abstract

Basal stem rot (BSR) disease caused by Ganoderma boninense is a major disease in oil palm plantations and there is no effective fungicide to control this disease. Several researchers have applied remote sensing for BSR studies, but, until now, WorldView-3 imagery has not been used to classify the severity of BSR disease symptoms. The objectives of this study were to predict disease severity with WorldView-3 imagery using supervised learning algorithms, and to describe the characteristic symptoms of BSR disease in oil palm at different disease severity levels that can be identified by WorldView-3 imagery. Field observation data were collected for 1923 oil palm trees with various levels of infection, namely healthy trees, and unhealthy trees with three levels of symptoms from mild to severe. Decision tree, random forest, and support vector machine learning algorithms were applied. The overall accuracy was low, but the accuracy was improved by about 1%–5% after outliers were removed from the data. Outliers were removed based on box plots of the distribution of reflectance value of band 4. Band 4 was selected by Jeffries-Matusita distance analysis and ‘stepclass’ algorithm. The separation of four levels of BSR disease described in this study was affected by the criteria used to describe the BSR disease symptoms in the field observation step. A redefined method and new criteria for BSR disease symptoms that can be identify using WorldView-3 imagery are described.