Random Forest classification model of basal stem rot disease caused by Ganoderma boninense in oil palm plantations

ABSTRACT

Ganoderma boninense is a fungus that causes basal stem rot (BSR) disease in oil palm plantations. BSR is a major disease in oil palm plantations in both Indonesia and Malaysia. There is no effective treatment for curing BSR; current treatments only prolong the life of oil palms. One strategy to control BSR is early detection of G. boninense infection. Based on the infection symptoms, many researchers have applied remote-sensing techniques for early detection and mapping of BSR disease in oil palms. The main objectives of this article were to evaluate the potential of machine-learning models for predicting BSR disease in oil palm plantations and to produce maps of the distribution of BSR disease. QuickBird imagery archived on 4 August 2008 was applied in three classifier models: Support Vector Machine, Random Forest (RF), and classification and regression tree models The RF model was best at predicting, classifying, and mapping oil palm BSR in terms of overall accuracy (OA), producer accuracy, user accuracy, and kappa value. Using 75% of the data for training and 25% for testing, the RF classifier model achieved 91% OA. In addition, this model separated the healthy and unhealthy oil palms in the study sites into 37,617 (75%) and 12,320 (25%) individuals, respectively.

Acknowledgements

The researchers acknowledge the Director of the Indonesian Oil Palm Research Institute for providing a grant for this study. The authors thank Professor Arthur Cracknell and the three anonymous referees for their useful comments, which helped us to improve the manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.