Review Update on the Life Cycle, Plant–Microbe Interaction, Genomics, Detection and Control Strategies of the Oil Palm Pathogen Ganoderma boninense

An In-Depth Study of Phytopathogenic Ganoderma: Pathogenicity, Advanced Detection Techniques, Control Strategies, and Sustainable Management

Phytopathogenic Ganoderma species pose a significant threat to global plant health, resulting in estimated annual economic losses exceeding USD (US Dollars) 68 billion in the agriculture and forestry sectors worldwide. To combat this pervasive menace effectively, a comprehensive understanding of the biology, ecology, and plant infection mechanisms of these pathogens is imperative. This comprehensive review critically examines various aspects of Ganoderma spp., including their intricate life cycle, their disease mechanisms, and the multifaceted environmental factors influencing their spread. Recent studies have quantified the economic impact of Ganoderma infections, revealing staggering yield losses ranging from 20% to 80% across various crops. In particular, oil palm plantations suffer devastating losses, with an estimated annual reduction in yield exceeding 50 million metric tons. Moreover, this review elucidates the dynamic interactions between Ganoderma and host plants, delineating the pathogen's colonization strategies and its elicitation of intricate plant defense responses. This comprehensive analysis underscores the imperative for adopting an integrated approach to Ganoderma disease management. By synergistically harnessing cultural practices, biological control, and chemical treatments and by deploying resistant plant varieties, substantial strides can be made in mitigating Ganoderma infestations. Furthermore, a collaborative effort involving scientists, breeders, and growers is paramount in the development and implementation of sustainable strategies against this pernicious plant pathogen. Through rigorous scientific inquiry and evidence-based practices, we can strive towards safeguarding global plant health and mitigating the dire economic consequences inflicted by Ganoderma infections.

Ganoderma Fusions with High Yield of Ergothioneine and Comparative Analysis of Its Genomics

Ergothioneine (EGT), an exceptional antioxidant found ubiquitously across diverse living organisms, plays a pivotal role in various vital physiological regulatory functions. Its principal natural sources are mushrooms and animal liver tissues. Ganoderma spp., a traditional Chinese food and medicinal mushroom, boasts high concentrations of EGT. To advance the development of novel Ganoderma spp. strains with enhanced EGT yields, we employed an efficient Ganoderma spp. protoplasmic fusion system. Through molecular and biological characterization, we successfully generated seven novel fusion strains. Notably, fusion strain RS7 demonstrated a remarkable increase in mycelial EGT production (12.70 ± 1.85 mg/L), surpassing the parental strains FQ16 and FQ23 by 34.23% and 39.10%, respectively. Furthermore, in the context of the fruiting body, fusion strain RS11 displayed a notable 53.58% enhancement in EGT production (11.24 ± 1.96 mg/L) compared to its parental strains. Genomic analysis of the RS7, the strain with the highest levels of mycelial EGT production, revealed mutations in the gene EVM0005141 associated with EGT metabolism. These mutations led to a reduction in non-productive shunts, subsequently redirecting more substrate towards the EGT synthesis pathway. This redirection significantly boosted EGT production in the RS7 strain. The insights gained from this study provide valuable guidance for the commercial-scale production of EGT and the selective breeding of Ganoderma spp. strains.

Exploring the potential of Bornean polypore fungi as biological control agents against pathogenic Ganoderma boninense causing basal stem rot in oil palm

Basal stem rot due to a fungal pathogen, Ganoderma boninense, is one of the most devastating diseases in oil palm throughout the major palm oil producer countries. This study investigated the potential of polypore fungi as biological control agents against pathogenic G. boninense in oil palm. In vitro antagonistic screening of selected non-pathogenic polypore fungi was performed. Based on in planta fungi inoculation on oil palm seedlings, eight of the 21 fungi isolates tested (GL01, GL01, RDC06, RDC24, SRP11, SRP12, SRP17, and SRP18) were non-pathogenic. In vitro antagonistic assays against G. boninense revealed that the percentage inhibition of radial growth (PIRG) in dual culture assay for SRP11 (69.7%), SRP17 (67.3%), and SRP18 (72.7%) was relatively high. Percentage inhibition of diameter growth (PIDG) in volatile organic compounds (VOCs) in dual plate assay of SRP11, SRP17, and SRP18 isolates were 43.2%, 51.6%, and 52.1%, respectively. Molecular identification using the internal transcribed spacer gene sequences of SRP11, SRP17, and SRP18 isolates revealed that they were Fomes sp., Trametes elegans, and Trametes lactinea, respectively.

Comparison of Ganoderma boninense Isolate's Aggressiveness Using Infected Oil Palm Seedlings

Basal stem rot incidence caused by a white-rot fungus, Ganoderma boninense, is the major disease of oil palm in Southeast Asia. The rate of disease transmission and host damage are affected by variations in pathogen aggressiveness. Several other studies have used the disease severity index (DSI) to determine G. boninense aggressiveness levels while verifying disease using a culture-based method, which might not provide accurate results or be feasible in all cases. To differentiate G. boninense aggressiveness, we employed the DSI and vegetative growth measurement of infected oil palm seedlings. Disease confirmation was performed through scanning electron microscopy and molecular identification of fungal DNA from both infected tissue and fungi isolated from Ganoderma selective medium. Two-month-old oil palm seedlings were artificially inoculated with G. boninense isolates (2, 4A, 5A, 5B, and 7A) sampled from Miri (Lambir) and Mukah (Sungai Meris and Sungai Liuk), Sarawak. The isolates were categorized into three groups: highly aggressive (4A and 5B), moderately aggressive (5A and 7A), and less aggressive (2). Isolate 5B was identified as the most aggressive, and it was the only one to result in seedling mortality. Out of the five vegetative growth parameters measured, only the bole size between treatments was not affected. The integration of both conventional and molecular approaches in disease confirmation allows for precise detection.

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

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.

Advances in Plant-Pathogen Interaction: New Challenges for Sustainable Disease Management

Plant pathogens cause huge losses and have been an important constraint to a worldwide increase in crop production and productivity [...].