Detection and classification of basal stem rot disease in oil palm using machine learning techniques: a mini review

Nur Azuan Husin, . and Mohd Hamim Abd Aziz, . and Siti Khairunniza Bejo, . and UNSPECIFIED (2023) Detection and classification of basal stem rot disease in oil palm using machine learning techniques: a mini review. Advances in Agricultural and Food Research Journal (AAFRJ) (Malaysia), 4 (2). pp. 1-21. ISSN 2735-1084

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.

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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.

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Item Type:	Article
AGROVOC Term:	Elaeis guineensis
AGROVOC Term:	plant diseases
AGROVOC Term:	classification
AGROVOC Term:	remote sensing
AGROVOC Term:	Fungi
AGROVOC Term:	artificial intelligence
AGROVOC Term:	early diagnosis
AGROVOC Term:	yield losses
Geographical Term:	Malaysia
Depositing User:	Ms. Azariah Hashim
Date Deposited:	10 Apr 2025 01:55
Last Modified:	10 Apr 2025 01:55
URI:	http://webagris.upm.edu.my/id/eprint/1380

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