Classification of basal stem rot using deep learning: a review of digital data collection and palm disease classification methods.

Abstract

Oil palm is a key agricultural resource in Malaysia. However, palm disease, most prominently basal stem rot caused at least RM 255 million of annual economic loss. Basal stem rot is caused by a fungus known as Ganoderma boninense . An infected tree shows few symptoms during early stage of infection, while potentially suffers an 80% lifetime yield loss and the tree may be dead within 2 years. Early detection of basal stem rot is crucial since disease control efforts can be done. Laboratory BSR detection methods are effective, but the methods have accuracy, biosafety, and cost concerns. This review article consists of scientific articles related to the oil palm tree disease, basal stem rot, Ganoderma Boninense , remote sensors and deep learning that are listed in the Web of Science since year 2012. About 110 scientific articles were found that is related to the index terms mentioned and 60 research articles were found to be related to the objective of this research thus included in this review article. From the review, it was found that the potential use of deep learning methods were rarely explored. Some research showed unsatisfactory results due to limitations on dataset. However, based on studies related to other plant diseases, deep learning in combination with data augmentation techniques showed great potentials, showing remarkable detection accuracy. Therefore, the feasibility of analyzing oil palm remote sensor data using deep learning models together with data augmentation techniques should be studied. On a commercial scale, deep learning used together with remote sensors and unmanned aerial vehicle technologies showed great potential in the detection of basal stem rot disease.

Plant phenotyping relevance

油ヤシの病害状態をリモートセンシングと深層学習で検出する手法群を対象としたレビューであり、植物病害フェノタイピング手法のレビューが中心である。

Code and data availability

This is a review article on basal stem rot detection using deep learning and remote sensing. It summarizes prior studies (TLS, SAR, tomography, thermal imaging) but provides no public phenotype datasets, images, author code, models, or supplements with availability statements. No qualifying paper-specific assets are in

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