Detection and Classification of Basal Stem Rot Disease in Oil Palm Using Machine Learning Techniques: A Mini Review

Abstract

The oil palm grown around the world to meet the demand for food and bio-fuels, is threatened by a fatal disease known as basal stem rot (BSR). Application of machine learning (ML) in agriculture keeps increasing with the advancement of technology, especially in disease detection. This manuscript presents a mini-review of the different methods relevant to BSR disease classification and detection using ML. The steps were discussed, including pre-processing and approaches used. Various algorithms, feature extractions and classification methods were discussed in the review. The review results revealed that the adoption of disease detection and classification methods for BSR disease in oil palm using ML approaches is still in its early stages of research. Hence, new tools are needed to fully automate the detection and classification processes for practical, operational, fast and accurate systems to be used in vast oil palm plantations.