Search Results (19)

Powdery mildew, caused by Erysiphe quercicola, is one of the primary diseases responsible for the reduction in natural rubber production in China. This disease is a typical airborne pathogen, characterized by its ability to spread via air currents and rapidly escalate into an epidemic under favorable environmental conditions. Accurate prediction and determination of the prevention and control period represent both a critical challenge and key focus area in managing rubber-tree powdery mildew. This study investigates the effects of spore concentration, environmental factors, and infection time on the progression of powdery mildew in rubber trees. By employing six distinct machine learning model construction methods, with the disease index of powdery mildew in rubber trees as the response variable and spore concentration, temperature, humidity, and infection time as predictive variables, a preliminary predictive model for the disease index of rubber-tree powdery mildew was developed. Results from indoor inoculation experiments indicate that spore concentration directly influences disease progression and severity. Higher spore concentrations lead to faster disease development and increased severity. The optimal relative humidity for powdery mildew development in rubber trees is 80% RH. At varying temperatures, the influence of humidity on the disease index differs across spore concentration, exhibiting distinct trends. Each model effectively simulates the progression of powdery mildew in rubber trees, with predicted values closely aligning with observed data. Among the models, the Kernel Ridge Regression (KRR) model demonstrates the highest accuracy, the R² values for the training set and test set were 0.978 and 0.964, respectively, while the RMSE values were 4.037 and 4.926, respectively. This research provides a robust technical foundation for reducing the labor intensity of traditional prediction methods and offers valuable insights for forecasting airborne forest diseases. Full article

Powdery mildew is a serious disease of the rubber tree (Hevea brasiliensis) worldwide. Temperature is the main climatic factor that influences the development of this disease. In this study, the effects of five high temperatures (30, 32, 34, 36, and 38 °C) at each of six exposure durations (0.5, 1, 3, 6, 12, and 24 h) were measured for the pathogen at 0, 3, 12, and 48 h post-inoculation (hpi), which represented four life stages of the fungus (conidia, conidial germination, infection, and hyphal growth). The results indicated that the germination, infection, and disease severity were reduced with increasing temperature and exposure duration. Temperature and exposure duration also significantly interacted to affect all life stages (p < 0.001). The relationships of the inhibition rate of conidial germination, infection, and disease severity with duration of exposure time (et) and high temperature (T) were described by logistic equations, with the percentage variance accounted for above 68%. Ungerminated conidia were found to be the most resistant to high temperature for E. quercicola from rubber tree, out of the four stages tested in this study. Only controlled-environmental experiments were conducted, and field studies are needed to enhance disease forecasting of rubber tree powdery mildew. Full article

The exocyst complex in eukaryotic cells modulates secretory vesicle transportation to promote exocytosis. The exocyst is also required for the hyphal growth and pathogenic development of several filamentous phytopathogens. Obligate biotrophic powdery mildew fungi cause considerable damage to many cash crops; however, the exocyst’s roles in this group of fungi is not well studied. To verify the functions of the exocyst in powdery mildew fungus, we identified two exocyst subunits, EqSec5 and EqSec6, from Erysiphe quercicola, a powdery mildew fungus that infects the rubber tree Hevea brasiliensis. When GFP-fused EqSec5 and EqSec6 were introduced into E. quercicola and another phytopathogenic fungus, Magnaporthe oryzae, they primarily localized to the hyphal tip region. Inducing gene silencing of EqSec5 or EqSec6 caused growth and infection defects, and those defects could not be fully restored under the NADPH oxidase inhibitor treatment to the plant. The silenced strains also induced the host defense response including reactive oxygen species accumulation and callose deposition. The silencing of EqSec5 or EqSec6 also inhibited the secretion of the effector protein EqIsc1, interrupting plant salicylic acid biosynthesis. Yeast two-hybrid and gene overexpression assays suggested that EqSec5 and EqSec6 interact with each other and can complement each other’s function during host infection. Overall, our study provides evidence that the exocyst in this powdery mildew fungus facilitates effector secretion, hyphal growth, and infection. Full article

Powdery mildew has become a significant disease affecting the yield and quality of rubber trees in recent years. It typically manifests on the leaf surface at an early stage, rapidly infecting and spreading throughout the leaves. Therefore, early detection and intervention are essential to reduce the resulting losses due to this disease. However, the conventional methods of disease detection are both time-consuming and labor-intensive. In this study, we proposed a novel deep-learning-based approach for detecting powdery mildew in rubber trees, even in complex backgrounds. First, to address the lack of existing datasets on rubber tree powdery mildew, we constructed a dataset comprising 6200 images and 38,000 annotations. Second, based on the YOLO framework, we integrated a multi-scale fusion module that combines a Feature Focus and Diffusion Mechanism (FFDM) into the neck of the detection architecture. We designed an overall focus diffusion architecture and introduced a Dimension-Aware Selective Integration (DASI) module to enhance the detection of small powdery mildew targets, naming the model PM-YOLO. Furthermore, we proposed an automatic grading detection algorithm to evaluate the severity of powdery mildew on rubber tree leaves. The experimental results demonstrated that the proposed method achieved 86.9% mean average precision (mAP) and 85.6% recall, which outperformed the standard YOLOv10 by 7.6% mAP and 8.2% recall. This approach offered accurate and real-time detection of powdery mildew rubber trees, providing an effective solution for early diagnosis through automated grading. Full article

Powdery mildew and anthracnose are the main diseases of rubber trees. In recent years, there have been large outbreaks in the rubber-planting areas of Asia, seriously affecting the yield and quality of rubber latex. ZAR1 is a conserved and distinctive coiled-coil nucleotide-binding leucine-rich (CNL) repeat in the plant kingdom, playing a crucial role in disease-resistance processes. To elucidate the function of the HbZAR1 gene in rubber trees (Hevea brasiliensis), three candidate HbZAR1 genes were identified using bioinformatics methods and comprehensively analyzed. The results indicate that the HbZAR1 protein is conserved in different plant species. Examination of cis-regulatory element sequences of HbZAR1genes reveals that the HbZAR1 gene promoter exhibits a remarkable enrichment of stress, light, and hormone elements. An expression analysis shows that the expression levels of the three HbZAR1 genes are highest in the bark and lowest in latex. Three HbZAR1 genes can respond to both rubber tree Erysiphe quercicola and Colletotrichum siamense infection; especially, HbZAR1.1 and HbZAR1.2 show significant upregulation in expression levels during the early stages of infection. These findings suggest that the three HbZAR1 genes may be involved in rubber tree susceptibility to E. quercicola and C. siamense through different immune mechanisms. Subcellular localization results indicate that the HbZAR1 genes are expressed in the nucleus and plasma membrane. This study also shows that the three HbZAR1 genes and activated mutant HbZAR1.1^D481V do not induce stable ROS production and cell death, suggesting possible gene degradation, functional redundancy, or acting as minor genes in disease resistance. This research provides valuable insights for further studying the function of HbZAR1 genes in rubber trees and the mechanisms of immune molecules. Full article

The majority of the world’s natural rubber comes from the rubber tree (Hevea brasiliensis). As a key enzyme for synthesizing phenylpropanoid compounds, phenylalanine ammonia-lyase (PAL) has a critical role in plant satisfactory growth and environmental adaptation. To clarify the characteristics of rubber tree PAL family genes, a genome-wide characterization of rubber tree PALs was conducted in this study. Eight PAL genes (HbPAL1-HbPAL8), which spread over chromosomes 3, 7, 8, 10, 12, 13, 14, 16, and 18, were found to be present in the genome of H. brasiliensis. Phylogenetic analysis classified HbPALs into groups I and II, and the group I HbPALs (HbPAL1-HbPAL6) displayed similar conserved motif compositions and gene architectures. Tissue expression patterns of HbPALs quantified by quantitative real-time PCR (qPCR) proved that distinct HbPALs exhibited varying tissue expression patterns. The HbPAL promoters contained a plethora of cis-acting elements that responded to hormones and stress, and the qPCR analysis demonstrated that abiotic stressors like cold, drought, salt, and H₂O₂-induced oxidative stress, as well as hormones like salicylic acid, abscisic acid, ethylene, and methyl jasmonate, controlled the expression of HbPALs. The majority of HbPALs were also regulated by powdery mildew, anthracnose, and Corynespora leaf fall disease infection. In addition, HbPAL1, HbPAL4, and HbPAL7 were significantly up-regulated in the bark of tapping panel dryness rubber trees relative to that of healthy trees. Our results provide a thorough comprehension of the characteristics of HbPAL genes and set the groundwork for further investigation of the biological functions of HbPALs in rubber trees. Full article

Powdery mildew significantly impacts the yield of natural rubber by being one of the predominant diseases that affect rubber trees. Accurate, non-destructive recognition of powdery mildew in the early stage is essential for the cultivation management of rubber trees. The objective of this study is to establish a technique for the early detection of powdery mildew in rubber trees by combining spectral and physicochemical parameter features. At three field experiment sites and in the laboratory, a spectroradiometer and a hand-held optical leaf-clip meter were utilized, respectively, to measure the hyperspectral reflectance data (350–2500 nm) and physicochemical parameter data of both healthy and early-stage powdery-mildew-infected leaves. Initially, vegetation indices were extracted from hyperspectral reflectance data, and wavelet energy coefficients were obtained through continuous wavelet transform (CWT). Subsequently, significant vegetation indices (VIs) were selected using the ReliefF algorithm, and the optimal wavelengths (OWs) were chosen via competitive adaptive reweighted sampling. Principal component analysis was used for the dimensionality reduction of significant wavelet energy coefficients, resulting in wavelet features (WFs). To evaluate the detection capability of the aforementioned features, the three spectral features extracted above, along with their combinations with physicochemical parameter features (PFs) (VIs + PFs, OWs + PFs, WFs + PFs), were used to construct six classes of features. In turn, these features were input into support vector machine (SVM), random forest (RF), and logistic regression (LR), respectively, to build early detection models for powdery mildew in rubber trees. The results revealed that models based on WFs perform well, markedly outperforming those constructed using VIs and OWs as inputs. Moreover, models incorporating combined features surpass those relying on single features, with an overall accuracy (OA) improvement of over 1.9% and an increase in F1-Score of over 0.012. The model that combines WFs and PFs shows superior performance over all the other models, achieving OAs of 94.3%, 90.6%, and 93.4%, and F1-Scores of 0.952, 0.917, and 0.941 on SVM, RF, and LR, respectively. Compared to using WFs alone, the OAs improved by 1.9%, 2.8%, and 1.9%, and the F1-Scores increased by 0.017, 0.017, and 0.016, respectively. This study showcases the viability of early detection of powdery mildew in rubber trees. Full article

Meteorological conditions play a crucial role in driving outbreaks of rubber tree powdery mildew (RTPM). As the climate warms and techniques improve, rubber cultivation is expanding to higher latitudes, and the changing climate increases the RTPM risk. Rubber plantations on Hainan Island, situated on the northern margin of the tropics, have been selected as a case study to explore the meteorological mechanisms behind RTPM outbreaks quantitatively using a structural equation model, and project current and future RTPM outbreak patterns under different climate change scenarios by building predictive models based on data-driven algorithms. The following results were obtained: (1) days with an average temperature above 20 °C and days with light rain were identified as key meteorological drivers of RTPM using structural equation modeling (R² = 0.63); (2) the Bayesian-optimized least-squares boosted trees ensemble model accurately predicted the interannual variability in the historical RTPM disease index (R² = 0.79); (3) currently, due to the increased area of rubber plantations in the central region of Hainan, there is a higher risk of RTPM; and (4) under future climate scenarios, RTPM shows a decreasing trend (at a moderate level), with oscillating and sporadic outbreaks primarily observed in the central and northwest regions. We attribute this to the projected warming and drying trends that are unfavorable for RTPM. Our study is expected to enhance the understanding of the impact of climate change on RTPM, provide a prediction tool, and underscore the significance of the climate-aware production and management of rubber. Full article

Powdery mildew is one of the most significant rubber tree diseases, with a substantial impact on the yield of natural rubber. This study aims to establish a detection approach that coupled continuous wavelet transform (CWT) and machine learning for the accurate assessment of powdery mildew severity in rubber trees. In this study, hyperspectral reflectance data (350–2500 nm) of healthy and powdery mildew-infected leaves were measured with a spectroradiometer in a laboratory. Subsequently, three types of wavelet features (WFs) were extracted using CWT. They were as follows: WFs dimensionally reduced by the principal component analysis (PCA) of significant wavelet energy coefficients (PCA-WFs); WFs extracted from the top 1% of the determination coefficient between wavelet energy coefficients and the powdery mildew disease class (1%R²-WFs); and all WFs at a single decomposition scale (SS-WFs). To assess the detection capability of the WFs, the three types of WFs were input into the random forest (RF), support vector machine (SVM), and back propagation neural network (BPNN), respectively. As a control, 13 optimal traditional spectral features (SFs) were extracted and combined with the same classification methods. The results revealed that the WF-based models all performed well and outperformed those based on SFs. The models constructed based on PCA-WFs had a higher accuracy and more stable performance than other models. The model combined PCA-WFs with RF exhibited the optimal performance among all models, with an overall accuracy (OA) of 92.0% and a kappa coefficient of 0.90. This study demonstrates the feasibility of combining CWT with machine learning in rubber tree powdery mildew detection. Full article

Plant PP2C genes are crucial for various biological processes. To elucidate the potential functions of these genes in rubber tree (Hevea brasiliensis), we conducted a comprehensive analysis of these genes using bioinformatics methods. The 60 members of the PP2C family in rubber tree were identified and categorized into 13 subfamilies. The PP2C proteins were conserved across different plant species. The results revealed that the HbPP2C genes contained multiple elements responsive to phytohormones and stresses in their promoters, suggesting their involvement in these pathways. Expression analysis indicated that 40 HbPP2C genes exhibited the highest expression levels in branches and the lowest expression in latex. Additionally, the expression of A subfamily members significantly increased in response to abscisic acid, drought, and glyphosate treatments, whereas the expression of A, B, D, and F1 subfamily members notably increased under temperature stress conditions. Furthermore, the expression of A and F1 subfamily members was significantly upregulated upon powdery mildew infection, with the expression of the HbPP2C6 gene displaying a remarkable 33-fold increase. These findings suggest that different HbPP2C subgroups may have distinct roles in the regulation of phytohormones and the response to abiotic and biotic stresses in rubber tree. This study provides a valuable reference for further investigations into the functions of the HbPP2C gene family in rubber tree. Full article

Rubber tree is one of the essential tropical economic crops, and rubber tree powdery mildew (PM) is the most damaging disease to the growth of rubber trees. Accurate and timely detection of PM is the key to preventing the large-scale spread of PM. Recently, unmanned aerial vehicle (UAV) remote sensing technology has been widely used in the field of agroforestry. The objective of this study was to establish a method for identifying rubber trees infected or uninfected by PM using UAV-based multispectral images. We resampled the original multispectral image with 3.4 cm spatial resolution to multispectral images with different spatial resolutions (7 cm, 14 cm, and 30 cm) using the nearest neighbor method, extracted 22 vegetation index features and 40 texture features to construct the initial feature space, and then used the SPA, ReliefF, and Boruta–SHAP algorithms to optimize the feature space. Finally, a rubber tree PM monitoring model was constructed based on the optimized features as input combined with KNN, RF, and SVM algorithms. The results show that the simulation of images with different spatial resolutions indicates that, with resolutions higher than 7 cm, a promising classification result (>90%) is achieved in all feature sets and three optimized feature subsets, in which the 3.4 cm resolution is the highest and better than 7 cm, 14 cm, and 30 cm. Meanwhile, the best classification accuracy was achieved by combining the Boruta–SHAP optimized feature subset and SVM model, which were 98.16%, 96.32%, 95.71%, and 88.34% at 3.4 cm, 7 cm, 14 cm, and 30 cm resolutions, respectively. Compared with SPA–SVM and ReliefF–SVM, the classification accuracy was improved by 6.14%, 5.52%, 12.89%, and 9.2% and 1.84%, 0.61%, 1.23%, and 6.13%, respectively. This study’s results will guide rubber tree plantation management and PM monitoring. Full article

Given that rubber is an important strategic material and the prevalence of rubber tree powdery mildew (RTPM) is a serious issue, the study of RTPM is becoming increasingly significant in aiding our understanding and managing rubber plantations. By enhancing our understanding, we may improve both the yield and quality of the rubber produced. Using meteorological station and reanalysis data, we employed factor expansion and three different feature-selection methods to screen for significant meteorological factors, ultimately constructing a data-driven RTPM disease index (RTPM-DI) model. This model was then used to analyze the spatiotemporal distribution of RTPM-DI in Hainan Island from 1980 to 2018, to reproduce and explore its patterns. The results show that (1) the RTPM-DI is dominantly negatively influenced by the average wind speed and positively affected by days with moderate rain; (2) the average wind speed and the days with moderate rain could explain 71% of the interannual variations in RTPM-DI, and a model established on the basis of these can simulate the changing RTPM-DI pattern very well (RMSE = 8.2511, MAE = 6.7765, MAPE = 0.2486, KGE = 0.9921, MSE = 68.081, RMSLE = 0.0953); (3) the model simulation revealed that during the period from 1980 to 2018, oscillating cold spots accounted for 72% of the whole area of Hainan Island, indicating a declining trend in RTPM-DI in the middle, western, southwestern, and northwestern regions. Conversely, new hot-spots and oscillating hot-spots accounted for 1% and 6% of the entire island, respectively, demonstrating an upward trend in the southeastern and northern regions. Additionally, no discernible pattern was observed for 21% of the island, encompassing the southern, eastern, and northeastern regions. It is evident that the whole island displayed significant spatial differences in the RTPM-DI pattern. The RTPM-DI model constructed in this study enhances our understanding of how climate change impacts RTPM, and it provides a useful tool for investigating the formation mechanism and control strategies of RTPM in greater depth. Full article

In order to trace the research history of diseases and pests in rubber tree and explore the potential for their natural control, a bibliometric analysis was conducted based on relevant documents retrieved from the Clarivate Analytics Web of Science (WoS) core collection SCI-E database. VOSviewer software was utilized to analyze the research distribution, scientific collaboration, knowledge structure, and research frontiers. The results show that annual publications on the diseases and pests of rubber tree have increased rapidly after 2005 after a long period of emergence and fluctuation. A total of 624 relevant publications from 51 countries/regions were identified. China was the most productive country with 152 documents, most of which were related to Colletotrichum leaf disease, powdery mildew, and other emerging diseases of rubber tree. France and Brazil produced rich research to tackle South American leaf blight, and have established a close collaborative relationship. Based on the analysis of themes and trend topics, pathogenicity mechanisms of fungal pathogens and plant defense mechanisms are currently hot topics. By further looking into the research, the defense-related genes of rubber tree and antagonistic mechanisms behind candidate biocontrol agents reveal great potential in developing natural control strategies. This study provides a useful reference about the progress and evolution of research into diseases and pests in rubber tree. Full article

Rubber tree powdery mildew (PM) is one of the most devastating leaf diseases in rubber forest plantations. To prevent and control PM, timely and accurate detection is essential. In recent years, unmanned Aerial Vehicle (UAV) remote sensing technology has been widely used in the field of agriculture and forestry, but it has not been widely used to detect forest diseases. In this study, we propose a method to detect the severity of PM based on UAV low-altitude remote sensing and multispectral imaging technology. The method uses UAVs to collect multispectral images of rubber forest canopies that are naturally infected, and then extracts 19 spectral features (five spectral bands + 14 vegetation indices), eight texture features, and 10 color features. Meanwhile, Pearson correlation analysis and sequential backward selection (SBS) algorithm were used to eliminate redundant features and discover sensitive feature combinations. The feature combinations include spectral, texture, and color features and their combinations. The combinations of these features were used as inputs to the RF, BPNN, and SVM algorithms to construct PM severity models and identify different PM stages (Asymptomatic, Healthy, Early, Middle and Serious). The results showed that the SVM model with fused spectral, texture, and color features had the best performance (OA = 95.88%, Kappa = 0.94), as well as the highest recognition rate of 93.2% for PM in early stages. Full article

The plant MYB transcription factor family featured as highly conserved DNA-binding domains consisting of 1 to 4 imperfect repeats (R). Increasing evidence indicates that MYB genes participates in growth, differentiation, metabolism, and biotic and abiotic stress responses. However, the functions of MYB genes in the rubber tree remain to be deeply elucidated, especially R2R3-MYB gene family. In this study, molecular biology, bioinformatics, and qRT-PCR were used to identify and analyze HbR2R3-MYB gene family members in the rubber tree. A total of 132 members of the R2R3-MYB gene family were identified in the rubber tree based on genome-wide level. Most of the HbR2R3-MYBs were mapped to 17 rubber tree chromosomes except four genes. A phylogenetic analysis divided all the HbR2R3-MYBs into 20 subgroups with Arabidopsis thaliana. MEME analysis showed that the protein of HbR2R3-MYBs was characterized by 9 conserved motifs. Twenty-six representative R2R3 HbMYBs from different subgroups were selected for expression profiles analysis and the results revealed that the HbR2R3-MYBs members showed various expression patterns in different tissues, powdery mildew-infected and ethylene treatment, implying the diversity of their functions in rubber trees. These results provide fundamental knowledge for further studying the response of the HbR2R3-MYB family to stress and regulation latex flow in rubber tree. Full article