Search Results (25)

Basal Stem Rot (BSR), caused by Ganoderma spp., is one of the most important emerging diseases of oil palm in Colombia and is so far restricted to only two producing areas in the country. However, despite the controls established to prevent its spread to new areas, containment has not been possible. This study aimed to understand BSR’s propagation mechanisms and related environmental conditions by measuring Ganoderma basidiospores’ concentrations at various heights using four 7-day Burkard volumetric samplers in a heavily affected plantation. Meteorological data, including solar radiation, temperature, humidity, precipitation, and wind speed, were also recorded. Analysis revealed higher basidiospore concentrations below 4 m, peaking at 02:00 h, with increased levels towards the study’s end. Spore concentrations were not directly influenced by temperature, humidity, or precipitation, but showed higher releases during drier periods. A significant correlation was found between wind speed and spore concentration, particularly below 1.5 m/s, though higher speeds might aid long-distance pathogen spread. This study highlights the complexity of BSR propagation and the need for continued monitoring and research to manage its impact on Colombia’s oil palm industry. Full article

Ganoderma boninense is a white-rot fungus that causes basal stem rot (BSR) disease in the oil palm. Potential natural inhibitors, such as gallic acid, thymol, propolis, and carvacrol, were assessed for their antagonistic effects against G. boninense. These naturally occurring phenolic compounds have also been utilised to inhibit hydrolytic and ligninolytic enzymes produced by the pathogen. Mycelial inhibition was dose-dependent in the presence of different concentrations of phenolic compounds, including, for example, in cellulase enzyme inhibition (GA mg/mL = 94%, THY 0.25 mg/mL = 90%, PRO 3.5 mg/mL = 92.5%, and CARV 0.15 mg/mL = 90.3%). A significant difference was observed revealing that gallic acid had the greatest inhibitory effect on the secretion of hydrolytic and ligninolytic enzymes, especially at 40 mM GA (cellulase = 0.337 U/mL, amylase = 0.3314 U/mL, xylanase = 0.211 U/mL, laccase = 0.4885 U/mL, lignin peroxidase = 0.218 U/mL, and manganese peroxidase = 0.386 U/mL). The growth and secretion of enzymes (inhibitory action) are inversely proportional to the concentration of phenolic compounds. Phenolic compounds have a greater potential as inhibitory agents and suppress the production of hydrolytic and ligninolytic enzymes. The selected phenolic compounds were evaluated for their ability to alter the morphology and integrity of G. boninense mycelia. The reduction in cell viability of G. boninense has been explained by research on morphological disruption, such as branching patterns, hyphal length, and rigidity of fungal cells, which eventually interrupt the secretion of enzymes. These studies highlight the efficacy of phenolic compounds in treating Ganoderma. In addition, these findings proved that naturally occurring phenolic compounds could be a substitute for chemical controls and other synthetic fungicides to eradicate the occurrence of BSR in oil palms, thus avoiding a situation that is difficult to overcome. Full article

Oil palms (OP) produce palm oil, a unique commodity without commercial alternatives. A serious disease of OP is basal stem rot (BSR) caused by Ganoderma boninense Pat. Climate change will likely increase BSR, thereby causing mortality of OP and reduced yields of palm oil. Work is being undertaken to produce modified OP (mOP) to resist BSR, although this will take decades for full development, if successfully produced at all. mOP will not be 100% effective, and it would be useful to know the effect of mOP on the key parameters of BSR incidence, OP mortality, and yield loss. The current paper employed CLIMEX modeling of suitable climates for OP and modeling narratives for Indonesia and Thailand. Indonesia is the largest producer of OP and Thailand is a much smaller manufacturer, and it was informative to compare these two countries. The gains from using mOP were substantial compared to the current production of some other continents and countries. The current paper, for the first time, assessed how climate change will affect BSR parameters for conventional and mOP. Greater consideration of the potential benefits of mOP is required to justify investing in the technology. Full article

Basal Stem Rot (BSR), a disease caused by Ganoderma boninense (G. boninense), has posed a significant concern for the oil palm industry, particularly in Southeast Asia, as it has the potential to cause substantial economic losses. The breeding programme is currently searching for G. boninense-resistant planting materials, which has necessitated intense manual screening in the nursery to track the progression of disease development in response to different treatments. The combination of hyperspectral image and machine learning approaches has a high detection potential for BSR. However, manual feature selection is still required to construct a detection model. Therefore, the objective of this study is to establish an automatic BSR detection at the seedling stage using a pre-trained deep learning model and hyperspectral images. The aerial view image of an oil palm seedling is divided into three regions in order to determine if there is any substantial spectral change across leaf positions. To investigate if the background images affect the performance of the detection, segmented images of the plant seedling have been automatically generated using a Mask Region-based Convolutional Neural Network (RCNN). Consequently, three models are utilised to detect BSR: a convolutional neural network that is 16 layers deep (VGG16) model trained on a segmented image; and VGG16 and Mask RCNN models both trained on the original images. The results indicate that the VGG16 model trained with the original images at 938 nm wavelength performed the best in terms of accuracy (91.93%), precision (94.32%), recall (89.26%), and F1 score (91.72%). This method revealed that users may detect BSR automatically without having to manually extract image attributes before detection. Full article

In recent years, oil palm has grown on a major scale as it is a prominent commodity crop that contributes the most to almost every producing country’s gross domestic product (GDP). Nonetheless, existing threats such as the Ganoderma basal stem rot (BSR) disease have been deteriorating the oil palm plantations and suitable actions to overcome the issue are still being investigated. The BSR disease progression in oil palm is being studied using the disease progression through the plant disease triangle idea. This concept looks at all potential elements that could affect the transmission and development of the disease. The elements include pathogenic, with their mode of infection in each studied factor. Full article

Ganoderma boninense is the major causal agent of basal stem rot (BSR) disease in oil palm, causing the progressive rot of the basal part of the stem. Despite its prominence, the key pathogenicity determinants for the aggressive nature of hemibiotrophic infection remain unknown. In this study, genome sequencing and the annotation of G. boninense T10 were carried out using the Illumina sequencing platform, and comparative genome analysis was performed with previously reported G. boninense strains (NJ3 and G3). The pan-secretome of G. boninense was constructed and comprised 937 core orthogroups, 243 accessory orthogroups, and 84 strain-specific orthogroups. In total, 320 core orthogroups were enriched with candidate effector proteins (CEPs) that could be classified as carbohydrate-active enzymes, hydrolases, and non-catalytic proteins. Differential expression analysis revealed an upregulation of five CEP genes that was linked to the suppression of PTI signaling cascade, while the downregulation of four CEP genes was linked to the inhibition of PTI by preventing host defense elicitation. Genome architecture analysis revealed the one-speed architecture of the G. boninense genome and the lack of preferential association of CEP genes to transposable elements. The findings obtained from this study aid in the characterization of pathogenicity determinants and molecular biomarkers of BSR disease. Full article

Early detection of Basal Stem Rot (BSR) disease in oil palms is an important plantation management activity in Southeast Asia. Practical approaches for the best strategic approach toward the treatment of this disease that originated from Ganoderma Boninense require information about the status of infection. In spite of the availability of conventional methods to detect this disease, they are difficult to be used in plantation areas that are commonly large in terms of planting hectarage; therefore, there is an interest for a quick and delicate technique to facilitate the detection and monitoring of Ganoderma in its early stage. The main goal of this paper is to evaluate the use of remote sensing technique for the rapid detection of Ganoderma-infected oil palms using Unmanned Aerial Vehicle (UAV) imagery integrated with an Artificial Neural Network (ANN) model. Principally, we sought for the most representative mean and standard deviation values from green, red, and near-infrared bands, as well as the best palm circle radius, threshold limit, and the number of hidden neurons for different Ganoderma severity levels. With the obtained modified infrared UAV images at 0.026 m spatial resolution, early BSR infected oil palms were most satisfactorily detected with mean and standard deviation derived from a circle radius of 35 pixels of band green and near-infrared, 1/8 threshold limit, and ANN network by 219 hidden neurons, where the total classification accuracies achieved for training and testing the dataset were 97.52% and 72.73%, respectively. The results from this study signified the utilization of an affordable digital camera and UAV platforms in oil palm plantation, predominantly in disease management. The UAV images integrated with the Levenberg–Marquardt training algorithm illustrated its great potential as an aerial surveillance tool to detect early Ganoderma-infected oil palms in vast plantation areas in a rapid and inexpensive manner. Full article

Basal stem rot (BSR), caused by Ganoderma spp., is one of the most important emerging oil palm diseases in Colombia, and is restricted to two oil palm production areas in the country. To identify the causal agent of the disease, basidiocarp of oil palms affected by BSR were used to prepare isolates, and their pathogenicity was then assessed in pre-nursery plants. Four-month-old oil palm seedlings were inoculated with rubber wood (Hevea brasiliensis) blocks colonized with dikaryotic mycelia of Ganoderma. The incidence, severity, and symptoms of the pathogen were assessed. A multiregional analysis (ITS, rpb2, and tef1-α) was carried out to identify the isolates; all isolates were determined to be Ganoderma zonatum. Phylogenetic analyses with the three regions yielded concordant phylogenetic information and supported the distinction of the isolates with high bootstrap support. Seven isolates (CPBsZN-01-29, CPBsZN-02-30, CPBsZN-03-31, CPBsZN-04-34, CPBsZN-05-35, CPBsZN-06-36, and CPBsZN-07-38) were pathogenic in oil palm, with incidences greater than 90% and a maximum severity of 34%, and the highest severity index was found in isolates CPBsZN-03-31, CPBsZN-04-34, and CPBsZN-06-36. The pathogen was recovered from inoculated oil palms in all cases. This study reveals the pathogenic association of Ganoderma zonatum with BSR in Colombia. Full article

Early detection of basal stem rot (BSR) disease in oil palm trees is important for the sustainable production of palm oil in the limited land for plantation in Southeast Asia. However, previous studies based on satellite and aircraft hyperspectral remote sensing could not discriminate oil palm trees in the early-stage of the BSR disease from healthy or late-stage trees. In this study, hyperspectral imaging of oil palm trees from an unmanned aerial vehicle (UAV) and machine learning using a random forest algorithm were employed for the classification of four infection categories of the BSR disease: healthy, early-stage, late-stage, and dead trees. A concentric disk segmentation was applied to tree crown segmentation at the sub-plant scale, and recursive feature elimination was used for feature selection. The results revealed that the classification performance for the early-stage trees is maximum at the specific tree crown segments, and only a few spectral bands in the red-edge region are sufficient to classify the infection categories. These findings will be useful for future UAV-based multispectral imaging to efficiently cover a wide area of oil palm plantations for the early detection of BSR disease. Full article

Plant pathogens are key threats to agriculture and global food security, causing various crop diseases that lead to massive economic losses. Palm oil is a commodity export of economic importance in Southeast Asia, especially in Malaysia and Indonesia. However, the sustainability of oil palm plantations and production is threatened by basal stem rot (BSR), a devastating disease predominantly caused by the fungus Ganoderma boninense Pat. In Malaysia, infected trees have been reported in nearly 60% of plantation areas, and economic losses are estimated to reach up to ~USD500 million a year. This review covers the current knowledge of the mechanisms utilized by G. boninense during infection and the methods used in the disease management to reduce BSR, including cultural practices, chemical treatments and antagonistic microorganism manipulations. Newer developments arising from multi-omics technologies such as whole-genome sequencing (WGS) and RNA sequencing (RNA-Seq) are also reviewed. Future directions are proposed to increase the understanding of G. boninense invasion mechanisms against oil palm. It is hoped that this review can contribute towards an improved disease management and a sustainable oil palm production in this region. Full article

In Malaysia, oil palm industry has made an enormous contribution to economic and social prosperity. However, it has been affected by basal stem rot (BSR) disease caused by Ganoderma boninense (G. boninense) fungus. The conventional practice to detect the disease is through manual inspection by a human expert every two weeks. This study aimed to identify the most suitable machine learning model to classify the inoculated (I) and uninoculated (U) oil palm seedlings with G. boninense before the symptoms’ appearance using hyperspectral imaging. A total of 1122 sample points were collected from frond 1 and frond 2 of 28 oil palm seedlings at the age of 10 months old, with 540 and 582 reflectance spectra extracted from U and I seedlings, respectively. The significant bands were identified based on the high separation between U and I seedlings, where the differences were observed significantly in the NIR spectrum. The reflectance values of each selected band were later used as input parameters of the 23 machine learning models developed using decision trees, discriminant analysis, logistic regression, naïve Bayes, support vector machine (SVM), k-nearest neighbor (kNN), and ensemble modelling with various types of kernels. The bands were optimized according to the classification accuracy achieved by the models. Based on the F-score and performance time, it was demonstrated that coarse Gaussian SVM with 9 bands performed better than the models with 35, 18, 14, and 11 bands. The coarse Gaussian SVM achieved an F-score of 95.21% with a performance time of 1.7124 s when run on a personal computer with an Intel^® Core™ i7-8750H processor and 32 GB RAM. This early detection could lead to better management in the oil palm industry. Full article

Basal stem rot (BSR) disease occurs due to the most aggressive and threatening fungal attack of the oil palm plant known as Ganoderma boninense (G. boninense). BSR is a disease that has a significant impact on oil palm crops in Malaysia and Indonesia. Currently, the only sustainable strategy available is to extend the life of oil palm trees, as there is no effective treatment for BSR disease. This study used thermal imagery to identify the thermal features to classify non-infected and BSR-infected trees. The aims of this study were to (1) identify the potential temperature features and (2) examine the performance of machine learning (ML) classifiers (naïve Bayes (NB), multilayer perceptron (MLP), and random forest (RF) to classify oil palm trees that are non-infected and BSR-infected. The sample size consisted of 55 uninfected trees and 37 infected trees. We used the imbalance data approaches such as random undersampling (RUS), random oversampling (ROS) and synthetic minority oversampling (SMOTE) in these classifications due to the different sample sizes. The study found that the T_max feature is the most beneficial temperature characteristic for classifying non-infected or infected BSR trees. Meanwhile, the ROS approach improves the curve region (AUC) and PRC results compared to a single approach. The result showed that the temperature feature T_max and combination feature T_maxT_min had a higher correct classification for the G. boninense non-infected and infected oil palm trees for the ROS-RF and had a robust success rate, classifying correctly 87.10% for non-infected and 100% for infected by G. boninense. In terms of model performance using the most significant variables, T_max, the ROS-RF model had an excellent receiver operating characteristics (ROC) curve region (AUC) of 0.921, and the precision–recall curve (PRC) region gave a value of 0.902. Therefore, it can be concluded that the ROS-RF, using the T_max, can be used to predict BSR disease with relatively high accuracy. Full article

Basal stem rot (BSR) disease of oil palm (Elaeis guineensis Jacq.) spreads through the contact of the plant roots with Ganoderma boninense (G. boninense) Pat. inoculum in the soil. The soil properties can be altered by growing seedlings with or without G. boninense inoculum. In the early stage of infection, the symptoms are difficult to detect. Therefore, an understanding of the environmental soil conditions of the plant is crucial in order to indicate the presence of the fungus. This paper presents an analysis of the temporal changes of the soil properties associated with the G. boninense infection in oil palm seedlings. A total of 40 seedlings aged five months were used in the study, comprising 20 inoculated (infected seedlings: IS) and 20 control (healthy seedlings: HS) seedlings. The seedlings were grown in a greenhouse for six months (24 weeks) under a controlled environmental temperature and humidity. The data of the soil moisture content (MC in %), electrical conductivity (EC in µS/cm), and temperature (T in °C) for each seedling were collected daily using three MEC10 soil sensors every hour and then transferred to the ThingSpeak cloud using a 3G Internet connection. Based on the results, the mean MC and EC showed a decreasing trend, while the mean T showed an increasing trend in both HS and IS during the six-month monitoring period. The overall mean in both the monthly and weekly analysis of MC, EC, and T was higher in HS than IS. However, in the monthly analysis, a Student’s t-test at a 5% significance level showed that only the soil MC and EC were significantly different between HS and IS, while in the weekly analysis, HS was significantly different from IS in all parameters. This study suggests that soil MC, EC, and T can be used as indicators of the G. boninense infection, especially for the weekly data. Full article

Basal stem rot (BSR) disease caused by pathogenic fungus Ganoderma boninense is a significant concern in the oil palm industry. G. boninense infection in oil palm induces defense-related genes. To understand oil palm defense mechanisms in response to fungal invasion, we analyzed differentially expressed genes (DEGs) derived from RNA-sequencing (RNA-seq) transcriptomic libraries of oil palm roots infected with G. boninense. A total of 126 DEGs were detected from the transcriptomic libraries of G. boninense-infected root tissues at different infection stages. Functional annotation via pathway enrichment analyses revealed that the DEGs were involved in the defense response against the pathogen. The expression of the selected DEGs was further confirmed using real-time quantitative PCR (qPCR) on independent oil palm seedlings and mature palm samples. Seven putative defense-related DEGs consistently showed upregulation in seedlings and mature plants during G. boninense infection. These seven genes might potentially be developed as biomarkers for the early detection of BSR in oil palm. Full article

Global increase in demand for palm oil has caused an intensification in oil palm plantation; however, production is greatly hindered by Basal Stem Rot (BSR) disease caused by Ganoderma boninense. There are many approaches to controlling BSR, although, there is no accurate, sustainable and effective method to suppress G. boninense completely. Hence, four phenolic compounds [Gallic acid (GA), Thymol (THY), Propolis (PRO) and Carvacrol (CARV)] were selected to evaluate their antifungal effect, ability to alter the mycelium morphology, and fungal cell integrity against G. boninense. Significant differences (p < 0.05) were observed and 94% of inhibition was exerted by GA on G. boninense growth. Scanning Electron Microscopy and High-Resolution Transmission Electron Microscopy observations revealed that GA and THY treatment caused severe damage to the mycelium and recorded the highest amount of sugar and electrolyte leakage. The study of cell integrity and morphological disruption has elucidated the reduction of G. boninense cell viability. Generally, our findings confirm the fungistatic effects of GA and THY. The evolution of phenolic compounds during the phytopathology studies indicated their coherence in eradicating the G. boninense. It is proposed that GA and THY had the potential to be developed further as a natural antifungal treatment to suppress G. boninense. Full article