From detection to decision: molecular, genomic, and AI innovations for managing Ganoderma boninense in oil palm

Ganoderma boninense causes basal stem rot (BSR) in oil palm, resulting in substantial economic losses, particularly for smallholders who contribute approximately 40% of global production. This review synthesizes advances in molecular diagnostic techniques (qPCR, LAMP), whole-genome sequencing of resistant Elaeis guineensis genotypes, and AI-driven remote sensing to enhance understanding of G. boninense pathobiology. Soilborne mycelia of the pathogen can persist for more than 10 y. Asymptomatic infections, which are frequently missed by conventional methods, can lead to significant disease spread within plantations over 2 y. CRISPR-based paper-strip tests have demonstrated high sensitivity (detecting < 1 pg DNA in laboratory settings), while marker-assisted selection has helped identify additional resistance QTLs. Trichoderma biofilms and biodegradable fumigants reduced soil inoculum by 50–75% in controlled studies. AI-based decision support systems have demonstrated promising accuracy for guiding interventions and potential cost savings, although further field validation across diverse conditions is needed.