Timing Fungicide Application Intervals Based on Airborne Erysiphe necator Concentrations

Triplex real-time qPCR for the simultaneous detection of Botryosphaeriaceae species in woody crops and environmental samples

Introduction

Species of Botryosphaeriaceae fungi are relevant pathogens of almond causing trunk cankers, extensive gumming, necrosis of internal tissues and plant dieback and dead, threatening almond productivity. A novel triplex quantitative real-time PCR (qPCR) assay was designed for the simultaneous detection and quantification of Neofusicoccum parvum, Botryosphaeria dothidea and the Botryosphaeriaceae family.

Material and methods

The method was validated in symptomatic and asymptomatic almond, avocado, blueberry and grapevine plants and in environmental samples, such as cropping soil and rainwater and in artificially inoculated trapped spores, demonstrating the same performance on several matrices.

Results and discussion

The limit of detection of the triplex qPCR was 10 fg of genomic DNA for the three fungal targets, with high correlation coefficients (R2) and amplification efficiencies between 90 and 120%. Although the triplex qPCR demonstrated to be more sensitive and accurate than the traditional plate culturing and further sequencing method, a substantial agreement (kappa index = 0.8052 ± 0.0512) was found between the two detection methods. The highly sensitive qPCR assay allows for accurate diagnosis of symptomatic plants and early detection of Botryosphaeriaceae fungi in asymptomatic plants (rootstocks and grafting scions from almond nurseries). Furthermore, the triplex qPCR successfully detected Botryosphaeriaceae fungi in environmental samples, such as cropping soils and rainwater. It was also capable of detecting as few as 10 conidia in artificially inoculated tapes. Therefore, the triplex qPCR is a valuable tool for accurate diagnosis, aiding in the implementation of suitable control measures. It enables preventive detection in asymptomatic samples, helping to avoid the introduction and spread of these pathogens in production fields. Moreover, it assists in identifying inoculum sources and quantifying inoculum levels in crop environments, contributing to a precise phytosanitary application schedule, thereby reducing production costs and preserving the environment.

AFM-YOLOv8s: An Accurate, Fast, and Highly Robust Model for Detection of Sporangia of Plasmopara viticola with Various Morphological Variants

Monitoring spores is crucial for predicting and preventing fungal- or oomycete-induced diseases like grapevine downy mildew. However, manual spore or sporangium detection using microscopes is time-consuming and labor-intensive, often resulting in low accuracy and slow processing speed. Emerging deep learning models like YOLOv8 aim to rapidly detect objects accurately but struggle with efficiency and accuracy when identifying various sporangia formations amidst complex backgrounds. To address these challenges, we developed an enhanced YOLOv8s, namely, AFM-YOLOv8s, by introducing an Adaptive Cross Fusion module, a lightweight feature extraction module FasterCSP (Faster Cross-Stage Partial Module), and a novel loss function MPDIoU (Minimum Point Distance Intersection over Union). AFM-YOLOv8s replaces the C2f module with FasterCSP, a more efficient feature extraction module, to reduce model parameter size and overall depth. In addition, we developed and integrated an Adaptive Cross Fusion Feature Pyramid Network to enhance the fusion of multiscale features within the YOLOv8 architecture. Last, we utilized the MPDIoU loss function to improve AFM-YOLOv8s' ability to locate bounding boxes and learn object spatial localization. Experimental results demonstrated AFM-YOLOv8s' effectiveness, achieving 91.3% accuracy (mean average precision at 50% IoU) on our custom grapevine downy mildew sporangium dataset-a notable improvement of 2.7% over the original YOLOv8 algorithm. FasterCSP reduced model complexity and size, enhanced deployment versatility, and improved real-time detection, chosen over C2f for easier integration despite minor accuracy trade-off. Currently, the AFM-YOLOv8s model is running as a backend algorithm in an open web application, providing valuable technical support for downy mildew prevention and control efforts and fungicide resistance studies.

It's a Trap! Part II: An Approachable Guide to Constructing and Using Rotating-Arm Air Samplers

An increasing number of researchers are looking to understand the factors affecting microbial dispersion but are often limited by the costs of commercially available air samplers. Some have reduced these costs by designing self-made versions; however, there are no published sampler designs, and there is limited information provided on the actual construction process. Lack of appropriate reference material limits the use of these self-made samplers by many researchers. This manuscript provides a guide to designing and constructing rotating-arm impaction air samplers by covering (i) environmental considerations, (ii) construction materials and equipment, (iii) the construction process, and (iv) air sampler deployment. Information regarding how to calculate rotational velocity, motor speed, and power supply requirements and to troubleshoot common issues is presented in an approachable format for individuals without experience in electronics or machining. Although many of the components discussed in this guide may change in their availability or be updated over time, this document is intended to serve as a "builder's guide" for future research into air sampling technology for phytopathology research.

A Rapid Glove-Based Inoculum Sampling Technique to Monitor Erysiphe necator in Commercial Vineyards

Information on the presence and severity of grape powdery mildew (GPM), caused by Erysiphe necator, has long been used to guide management decisions. While recent advances in the available molecular diagnostic assays and particle samplers have made monitoring easier, there is still a need for more efficient field collection of E. necator. The use of vineyard worker gloves worn during canopy manipulation as a sampler (glove swab) of E. necator was compared with samples identified by visual assessment with subsequent molecular confirmation (leaf swabs) and airborne spore samples collected by rotating-arm impaction traps (impaction traps). Samples from United States commercial vineyards in Oregon, Washington, and California were analyzed using two TaqMan qPCR assays targeting the internal transcribed spacer regions or cytochrome b gene of E. necator. Based on qPCR assays, visual disease assessments misidentified GPM up to 59% of the time with a higher frequency of misidentification occurring earlier in the growing season. Comparison of the aggregated leaf swab results for a row (n = 915) to the row's corresponding glove swab had 60% agreement. The latent class analysis (LCA) indicated that glove swabs were more sensitive than leaf swabs in detecting E. necator presence. The impaction trap results had 77% agreement to glove swabs (n = 206) taken from the same blocks. The LCAs estimated that the glove swabs and impaction trap samplers varied each year in which was more sensitive for detection. This likely indicates that these methods have similar levels of uncertainty and provide equivalent information. Additionally, all samplers, once E. necator was detected, were similarly sensitive and specific for detection of the A-143 resistance allele. Together, these results suggest that glove swabs are an effective sampling method for monitoring the presence of E. necator and, subsequently, the G143A amino acid substitution associated with resistance to quinone outside inhibitor fungicides in vineyards. Glove swabs could reduce sampling costs due to the lack of need for specialized equipment and time required for swab collection and processing.

Efficiency of inorganic fungicides against the formation of Erysiphe necator chasmothecia in vineyards

BACKGROUND

A reduction in chasmothecia, an important inoculum of grape powdery mildew (Erysiphe necator Schwein.), is essential for disease control in vineyards; the use of fungicides during the formation of chasmothecia on vine leaves, late in the growing season, may accomplish this. Inorganic fungicides, such as sulphur, copper, and potassium bicarbonate, are very useful for this purpose because of their multisite mode of action. The aim of this study was to evaluate chasmothecia reduction using different fungicide applications late in the growing season in commercially managed vineyards and in an exact application trial.

RESULTS

Chasmothecia on vine leaves were reduced in commercial vineyards by four copper (P = 0.01) and five potassium bicarbonate (P = 0.026) applications. The positive effect of potassium bicarbonate was also confirmed in the application trial, where two applications showed lower chasmothecia numbers than the control (P = 0.002).

CONCLUSION

The application of inorganic fungicides reduced the amount of chasmothecia as the primary inoculum source. Potassium bicarbonate and copper are of further interest for disease control as these fungicides can be used by organic and conventional wine growers. The application of these fungicides should be carried out as late as possible before harvest to reduce chasmothecia formation and, consequently, the potential for powdery mildew infestation in the subsequent season. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Current Trends and Perspectives on Predictive Models for Mildew Diseases in Vineyards

Environmental and economic costs demand a rapid transition to more sustainable farming systems, which are still heavily dependent on chemicals for crop protection. Despite their widespread application, powdery mildew (PM) and downy mildew (DM) continue to generate serious economic penalties for grape and wine production. To reduce these losses and minimize environmental impacts, it is important to predict infections with high confidence and accuracy, allowing timely and efficient intervention. This review provides an appraisal of the predictive tools for PM and DM in a vineyard, a specialized farming system characterized by high crop protection cost and increasing adoption of precision agriculture techniques. Different methodological approaches, from traditional mechanistic or statistic models to machine and deep learning, are outlined with their main features, potential, and constraints. Our analysis indicated that strategies are being continuously developed to achieve the required goals of ease of monitoring and timely prediction of diseases. We also discuss that scientific and technological advances (e.g., in weather data, omics, digital solutions, sensing devices, data science) still need to be fully harnessed, not only for modelling plant-pathogen interaction but also to develop novel, integrated, and robust predictive systems and related applied technologies. We conclude by identifying key challenges and perspectives for predictive modelling of phytopathogenic disease in vineyards.

Clade-Specific Monitoring of Airborne Pseudoperonospora spp. Sporangia Using Mitochondrial DNA Markers for Disease Management of Cucurbit Downy Mildew

Management of cucurbit downy mildew (CDM) caused by Pseudoperonospora cubensis, relies on an intensive fungicide program. In Michigan, CDM occurs annually due to an influx of airborne sporangia and timely alerts of airborne inoculum can assist growers in assessing the need to initiate fungicide sprays. This research aimed to improve the specific detection of airborne P. cubensis sporangia by adapting quantitative real-time polymerase chain reaction (qPCR) assays to distinguish among P. cubensis clades I and II and P. humuli in spore trap samples from commercial production sites and research plots. We also evaluated the suitability of impaction spore traps compared with Burkard traps for detection of airborne sporangia. A multiplex qPCR assay improved the specificity of P. cubensis clade II detection accelerating the assessment of field spore trap samples. After 2 years of monitoring, P. cubensis clade II DNA was detected in spore trap samples before CDM symptoms were first observed in cucumber fields (July and August), while P. cubensis clade I DNA was not detected in air samples before or after the disease onset. In some commercial cucumber fields, P. humuli DNA was detected throughout the growing season. The Burkard spore trap appeared to be better suited for recovery of sporangia at low concentrations than the impaction spore trap. This improved methodology for the monitoring of airborne Pseudoperonospora spp. sporangia could be used as part of a CDM risk advisory system to time fungicide applications that protect cucurbit crops in Michigan.

Duration of Downy Mildew Control Achieved with Fungicides on Cucumber Under Florida Field Conditions

Cucurbit production in Florida is impacted by downy mildew on a yearly basis. Cucurbit downy mildew (CDM), caused by Pseudoperonospora cubensis, is one of the most devastating cucurbit diseases and can lead to complete yield loss. Nearly continuous production of cucurbits occurs temporally throughout Florida, which puts extensive pressure on the pathogen population to select for individuals that are resistant to fungicides in use labeled for CDM. Loss of efficacy as a result of fungicide resistance developing is becoming a major concern for Florida cucurbit growers who rely on these products to manage CDM. This study was established to evaluate the field activity of 11 utilized fungicides by determining their duration of activity when applied at various intervals for the management of CDM in cucumber under Florida field conditions. By comparing levels of percent CDM control and area under the disease progress curve values, the fungicide's duration of field activity was established. Field activities were <1 week for dimethomorph and fluopicolide; 1 week for cymoxanil; 1 to 2 weeks for chlorothalonil and mancozeb; 2 weeks for ethaboxam; 1 to 3 weeks for propamocarb, cyazofamid, and ametoctradin + dimethomorph; and 2 to 4 weeks for oxathiapiprolin and fluazinam. Knowledge of duration of field activity can potentially improve the development of CDM management programs and slow the resistance selection.

Allele-Specific Detection Methods for QoI Fungicide-Resistant Erysiphe necator in Vineyards

Grapevine powdery mildew (GPM), caused by the fungus Erysiphe necator, is a constant threat to worldwide production of grape berries, requiring repeated use of fungicides for management. The frequent fungicide applications have resulted in resistance to commonly used quinone outside inhibitor (QoI) fungicides and the resistance is associated with single-nucleotide polymorphisms (SNPs) in the mitochondrial cytochrome b gene (cytb). In this study, we attempted to detect the most common SNP causing a glycine to alanine substitution at amino acid position 143 (i.e., G143A) in the cytb protein, to track this resistance using allele-specific TaqMan probe and digital-droplet PCR-based assays. Specificity and sensitivity of these assays showed that these two assays could discriminate SNPs and were effective on mixed samples. These diagnostic assays were implemented to survey E. necator samples collected from leaf and air samples from California and Oregon grape-growing regions. Sequencing of PCR amplicons and phenotyping of isolates also revealed that these assays accurately detected each allele (100% agreement), and there was an absolute agreement between the presence or absence of the G143A mutation and resistance to QoIs in the E. necator sampled. These results indicate that the developed diagnostic tools will help growers make informed decisions about fungicide selections and applications which, in turn, will facilitate GPM disease management and improve grape production systems.

Can Spore Sampler Data Be Used to Predict Plasmopara viticola Infection in Vineyards?

Grapevine downy mildew (DM) is caused by the dimorphic oomycete Plasmopara viticola, which incites epidemics through primary and secondary infection cycles that occur throughout the season. The secondary infection cycles are caused by the sporangia produced on DM lesions. The current research examined the relationship between numbers of airborne sporangia and DM development on grape leaves to determine whether spore sampler data can be useful to predict the potential for secondary infections of P. viticola. Three years (2015-2017) of spore sampler data confirmed that sporangia are a common component of the airborne microflora in a DM-infested vineyard and that their numbers depend on weather conditions. For a total of 108 days, leaf samples were collected from the vineyard at 2- to 3-day intervals and incubated under optimal conditions for P. viticola infection. The numbers of airborne sporangia sampled on 1 to 7 days before leaf sampling were significantly correlated with the numbers of DM lesions on the leaves. The best correlation (r=0.59), however, was found for the numbers of viable airborne sporangia (SPV), which were assessed by using equations driven by the vapour pressure deficit. In Bayesian and ROC curve analyses, SPV was found to be a good predictor of P. viticola infection of grape leaves, with AUROC=0.821 and false positive predictions mainly occurring at low SPV. A binary logistic regression showed that a threshold of 2.52 viable sporangia m^-3 air day^-1 enables a prediction of no infection with a posterior probability of 0.870, which was higher than the prior probability of 0.574. Numbers of viable sporangia in the vineyard air is therefore a useful predictor of infection and especially of no infection. The predictor missed some observed infections, but these infections were not severe (they accounted for only 31 of 374 DM lesions).

Establishment of a Rapid Detection Method for Rice Blast Fungus Based on One-Step Loop-Mediated Isothermal Amplification (LAMP)

Rice blast is one of the most serious diseases for rice, and controlling the filamentous fungus Magnaporthe oryzae that causes rice blast is crucial for global food security. Typically, early infected rice does not show symptoms. Therefore, the early diagnosis of rice blast is particularly important to avoid uncontrollable propagation of rice blast fungus. In the present work, a rapid and efficient loop-mediated isothermal amplification (LAMP) method was developed to detect the pathogen at the early infected stage of rice. The Alb1 superfamily hypothetical protein MGG_04322, a nuclear shuttling factor involved in ribosome and melanin biogenesis, was chosen as the target for designing the LAMP primers. The LAMP assay enabled rapid detection of as little as 10 pg of pure genomic DNA of M. oryzae. In addition, we established the quantitative LAMP (q-LAMP) detection system to quantify the conidia of rice blast fungus. The q-LAMP assay enabled rapid detection (within 35 min) of the fungal spores at a sensitivity of 3.2 spores/ml. In addition, the assay sets up the linearization formula of the standard curve as y = 0.3066 + 15.33x (where x = amplification of time), inferring that spore number = 10^0.60y. In addition, the q-LAMP assay was successfully used to detect the presence of the virulence strains of M. oryzae (wild type) in comparison with that of the two mutant strains by quantifying the biomass within host tissue. These results provide a useful and convenient tool for detecting M. oryzae that could be applied in the incubation period of rice blast before symptoms appear.

Development of a quantitative loop-mediated isothermal amplification assay for the field detection of Erysiphe necator

Plant pathogen detection systems have been useful tools to monitor inoculum presence and initiate management schedules. More recently, a loop-mediated isothermal amplification (LAMP) assay was successfully designed for field use in the grape powdery mildew pathosystem; however, false negatives or false positives were prevalent in grower-conducted assays due to the difficulty in perceiving the magnesium pyrophosphate precipitate at low DNA concentrations. A quantitative LAMP (qLAMP) assay using a fluorescence resonance energy transfer-based probe was assessed by grape growers in the Willamette Valley of Oregon. Custom impaction spore samplers were placed at a research vineyard and six commercial vineyard locations, and were tested bi-weekly by the lab and by growers. Grower-conducted qLAMP assays used a beta-version of the Smart-DART handheld LAMP reaction devices (Diagenetix, Inc., Honolulu, HI, USA), connected to Android 4.4 enabled, Bluetooth-capable Nexus 7 tablets for output. Quantification by a quantitative PCR assay was assumed correct to compare the lab and grower qLAMP assay quantification. Growers were able to conduct and interpret qLAMP results; however, the Erysiphe necator inoculum quantification was unreliable using the beta-Smart-DART devices. The qLAMP assay developed was sensitive to one spore in early testing of the assay, but decreased to >20 spores by the end of the trial. The qLAMP assay is not likely a suitable management tool for grape powdery mildew due to losses in sensitivity and decreasing costs and portability for other, more reliable molecular tools.