Disease Of Grapevine Research Articles

Forecasting the phytosanitary state of agrocenoses: innovation developments

Goal. Development and implementation of new theories, methods, technologies and models that would allow more reliable forecasting of the development of pests and pathogens of agricultural crops.
 Methods. Analysis of innovative development of the Institute of Plant Protection of the National Academy of Agrarian Sciences of Ukraine and other institutions of the Scientific and Methodological Center «Protection of Plants» for 2001—2020. Isolation of those relating to the problem forecasting the development of pests and pathogens of plant diseases in agrocenoses of Ukraine.
 Results. The concept of formation of the phytosanitary state of crops and plantations under the conditions of climate change has been developed. The theory of the dynamics of the number of locusts and the prediction of emergency situations in the agricultural sphere of Ukraine has been developed. The widespread use of pheromone traps for diagnosing the phytosanitary state of agrocenoses of Ukraine is recommended. The cyclicity of outbreaks of mass reproduction of the Anisoplia austriaca beetle and the European corn borer was established, depending on the amount of solar activity. The possibility of applying GIS-technologies for the analysis of long-term data was studied. Predictive computer programs (models) of harvest shortages of rapeseed, sunflower, corn, sugar beet, and winter wheat from a complex of pests have been developed. An algorithm for the application of information technology programs for the analysis of multi-year dynamics of the phytosanitary state of agrocenoses has been developed. The role of the developed computer model of the development of leaf spots of grain crops in determining the moment for the most effective application of fungicides has been established. Methods of forecasting the development of pathogens of grape diseases in the South of Ukraine have been developed. A system of forecasting the phytosanitary state of agrocenoses of Ukraine in the conditions of the year and planning the implementation of protective measures has been developed.
 Conclusions. Innovative developments of the Scientific and Methodological Center «Plant Protection» make it possible to solve the following issues at a modern level: forecasting changes in the agrosphere based on the analysis of a long-term database of hydrothermal conditions and indicators of the phytosanitary state of agrocenoses; prevention of emergency situations in the agricultural sector on the basis of a modern monitoring system using GPS-navigation and the development of regulations for carrying out protective measures; forecasting the development of dangerous pests and diseases of agricultural crops; forecasting crop failures and determining the economic feasibility of chemical plant protection; forecasting the appearance and spread of quarantine pests.

Control of Pierce's Disease of Grape with Paraburkholderia phytofirmans PsJN in the Field.

Replicated field studies were conducted to evaluate the factors that could influence the efficacy of Paraburkholderia phytofirmans PsJN for the control of Pierce's disease of grape, as well as to determine the extent to which disease control was systemic within plants. Topical applications of PsJN with an organosilicon surfactant was an effective way to introduce this bacterium under field conditions and provided similar levels of disease control as its mechanical inoculation. Disease incidence in inoculated shoots was often reduced two- to threefold when PsJN was inoculated a single time as much as 3 weeks before Xylella fastidiosa and up to 5 weeks after the pathogen. Inoculation of a shoot with PsJN greatly decreased the probability of any symptoms rather than reducing the severity of disease, suggesting a systemic protective response of individual shoots. Although the likelihood of disease symptoms on shoots inoculated with the pathogen on PsJN-treated plants was lower than on control plants inoculated only with the pathogen, the protection conferred by PsJN was not experienced by all shoots on a given plant. This suggested that any systemic resistance was spatially limited. Whereas the population size of PsJN increased to more than 106 cells/g and spread more than 1 m within 12 weeks after its inoculation alone into grape, its population size subsequently decreased greatly after about 5 weeks, and its distal dispersal in stems was restricted when co-inoculated with X. fastidiosa. PsJN may experience collateral damage from apparent host responses induced when both species are present.

Variations Among Crop Seasons and Inoculum Availability Have a Higher Impact on Grapevine Rust Epidemics than Training Systems or Plastic Cover.

Canopy management practices can be effective as part of the integrated management of grapevine diseases. This study aimed to determine whether training systems and plastic covers can contribute to prevent Asian grapevine leaf rust (AGLR) development. Additionally, the influence of crop season and inoculum availability on AGLR development was investigated. Six-season experiments were carried out to characterize 16 epidemics that developed from natural inoculum (NI) or artificial inoculum (AI) sources (NI + AI), conducted in different training systems and with or without the plastic cover. The Richards model was fitted to each AGLR disease progress curve to estimate and compare the onsets and intensities of epidemics using eight curve elements. Principal components analysis (PCA) identified the incidence progress rate, the area under the severity progress curve, final disease severity, time to disease onset, and time to reach the inflection point as the main descriptors for AGLR epidemics. The results showed that AGLR epidemic development was related mainly to differences in inoculum availability and climatic conditions throughout the seasons and to a lower extent to the training system and plastic cover. The earliest disease onset was observed in epidemics when the NI was supplemented with an AI source. Differences in AGRL intensity were correlated to accumulated precipitation, being less severe in autumn-winter than in the spring-summer season. The present findings provided a better understanding of the structure and the seasonal variation of AGLR in cultivar 'Niagara Rosada'. The strategies for reducing and/or delaying inoculum buildup among seasons are discussed.

Fatty Acid Methyl Ester Profiling of Californian Xylella fastidiosa Strains

Xylella fastidiosa ssp. fastidiosa ( Xff) is the causal agent of Pierce's disease of grapevine, a management-intensive and potentially deadly disease. However, different stains and other subspecies, such as Xylella fastidiosa ssp. multiplex ( Xfm), exist in the same regions and vary in capacity to cause disease. All strains differ in the fatty acids that comprise cell membranes, as these allow adaptations to specific host microenvironments. Therefore, studies were initiated to observe the fatty acid profiles of different Californian Xf isolates via fatty acid methyl ester (FAME) analysis. Observations revealed that the four Xff strains had similar FAME profiles that were distinct from those of the three Xfm strains, even in isolates that originated from the same host plant species. These data show consistent differences between Xff and Xfm strains and demonstrate the potential that FAME profiling has for Xylella subspecies identification of novel isolates. [Formula: see text] The author(s) have dedicated the work to the public domain under the Creative Commons CC0 “No Rights Reserved” license by waiving all of his or her rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law, 2024.

Early Prediction of Plant Disease ESCA

This research revolutionizes grapevine security worldwide and sustains premium wine production by using CNN-driven algorithms and different datasets to pioneer multimodal detection for early Esca disease in grapevines. Also give a model with more accuracy so that we can predict this plant disease early. Global grapevine output is being threatened by the complicated fungal illness known as esca disease, which also threatens the stability of the economy and the quality of premium wines. The capacity of current detection techniques to detect Esca to detect the disease early is restricted and frequently imprecise. By utilizing the capabilities of Convolutional Neural Networks (CNNs) and a variety of datasets, this study offers a novel method that develops multimodal detection for early Esca diagnosis. Compared to traditional methods, our model achieves higher accuracy by combining spectral and temporal information with visual imaging of leaves and stems. Key Words: CNN, ESCA, Machine Learning, Plant Disease

Real-time and lightweight detection of grape diseases based on Fusion Transformer YOLO.

Grapes are prone to various diseases throughout their growth cycle, and the failure to promptly control these diseases can result in reduced production and even complete crop failure. Therefore, effective disease control is essential for maximizing grape yield. Accurate disease identification plays a crucial role in this process. In this paper, we proposed a real-time and lightweight detection model called Fusion Transformer YOLO for 4 grape diseases detection. The primary source of the dataset comprises RGB images acquired from plantations situated in North China. Firstly, we introduce a lightweight high-performance VoVNet, which utilizes ghost convolutions and learnable downsampling layer. This backbone is further improved by integrating effective squeeze and excitation blocks and residual connections to the OSA module. These enhancements contribute to improved detection accuracy while maintaining a lightweight network. Secondly, an improved dual-flow PAN+FPN structure with Real-time Transformer is adopted in the neck component, by incorporating 2D position embedding and a single-scale Transformer Encoder into the last feature map. This modification enables real-time performance and improved accuracy in detecting small targets. Finally, we adopt the Decoupled Head based on the improved Task Aligned Predictor in the head component, which balances accuracy and speed. Experimental results demonstrate that FTR-YOLO achieves the high performance across various evaluation metrics, with a mean Average Precision (mAP) of 90.67%, a Frames Per Second (FPS) of 44, and a parameter size of 24.5M. The FTR-YOLO presented in this paper provides a real-time and lightweight solution for the detection of grape diseases. This model effectively assists farmers in detecting grape diseases.

Mixed zeros inflated poisson model: an application in grape disease control

Longitudinal data studies describe the behavior of a feature of observations over time through so-called mixed models, which bear this name because they contain fixed effects and random effects. Still, if many of the observations do not show such a characteristic, i.e., the amount of zeros observed is greater than expected, then we say that it is inflated data of zeros. This work aims to describe the applicability of a mixed zero inflated Poisson (ZIP) model to model the temporal progress of mildew (Plasmopara viticultura), which corresponds to fungal disease of the grape, more specifically in the cultivar BRS Vitória, and assess its tolerance for 6 different treatments. This is intended to evaluate the quality of the proposed model fit and to carry out tests comparing the treatment averages to point out which treatments are equally effective in the control of mildew, thus demonstrating the application of the mixed ZIP model in a real and significant problem. A mixed ZIP model was constructed based on methodologies found in the literature and for inference and estimation of the parameters the method of maximum likelihood was used. The results showed that the mixed ZIP model adjusted well to the data, and hypothesis testing for average pointed to equality in efficacy between some treatment groups, thus making it possible to obtain a treatment with fewer sprays within each group.

An Exploration of Deep Learning Techniques for the Detection of Grape Diseases

Abstract: Plant diseases are one of the major contributors to economic loss in the agriculture industry worldwide. Detection of disease at early stages can help in the reduction of this loss. In recent times, a lot of emphasis has been done on disease detection due to the overall increase in production as well as the loss of grape number. With deep learning, having a promising future and having the advantages of automatic learning and feature extraction, the use of these techniques has now been widely spread. This paper reviewed the existing deep-learning techniques available for grape disease detection. Firstly, covering the various steps in a grape disease detection model ranging from the various sources of image acquisition, the different image augmentation techniques and the various models used, and the parameters required to evaluate. Secondly, the study summarizes the important findings of all literature available on the theme. The paper also tries to highlight the various challenges faced by the researchers and the common trend among them, so that future research on the topic can achieve higher performance.

The Exometabolome of Xylella fastidiosa in Contact with Paraburkholderia phytofirmans Supernatant Reveals Changes in Nicotinamide, Amino Acids, Biotin, and Plant Hormones.

Microbial competition within plant tissues affects invading pathogens' fitness. Metabolomics is a great tool for studying their biochemical interactions by identifying accumulated metabolites. Xylella fastidiosa, a Gram-negative bacterium causing Pierce's disease (PD) in grapevines, secretes various virulence factors including cell wall-degrading enzymes, adhesion proteins, and quorum-sensing molecules. These factors, along with outer membrane vesicles, contribute to its pathogenicity. Previous studies demonstrated that co-inoculating X. fastidiosa with the Paraburkholderia phytofirmans strain PsJN suppressed PD symptoms. Here, we further investigated the interaction between the phytopathogen and the endophyte by analyzing the exometabolome of wild-type X. fastidiosa and a diffusible signaling factor (DSF) mutant lacking quorum sensing, cultivated with 20% P. phytofirmans spent media. Liquid chromatography-mass spectrometry (LC-MS) and the Method for Metabolite Annotation and Gene Integration (MAGI) were used to detect and map metabolites to genomes, revealing a total of 121 metabolites, of which 25 were further investigated. These metabolites potentially relate to host adaptation, virulence, and pathogenicity. Notably, this study presents the first comprehensive profile of X. fastidiosa in the presence of a P. phytofirmans spent media. The results highlight that P. phytofirmans and the absence of functional quorum sensing affect the ratios of glutamine to glutamate (Gln:Glu) in X. fastidiosa. Additionally, two compounds with plant metabolism and growth properties, 2-aminoisobutyric acid and gibberellic acid, were downregulated when X. fastidiosa interacted with P. phytofirmans. These findings suggest that P. phytofirmans-mediated disease suppression involves modulation of the exometabolome of X. fastidiosa, impacting plant immunity.

Bridging boundaries: Exploring vineyard, management and variety characteristics influencing long-term infection of grapevine pathogens

Globalisation, climatic changes, and increasing consumer demand have forced the intensification of agricultural production. Thus, vineyards have crossed the boundaries of the original production zones and extended towards suboptimal areas, increasing the potential risk of damaging disease outbreaks. Therefore, there is a rising need for a complex and empirical revision of the interfering effects between grape infection and other external, large-scale factors such as environmental conditions and management practices. Although external abiotic and biotic factors could determine the infection levels of grape disease in a complex way, existing studies focus on the short-term effects of only a single or very few potential factors. In this large-scale study, we aimed to reveal the long-term impact of specific factors regarding vineyard characteristics, applied crop management and grape variety features, which could determine the infection severity of primary grape diseases (grey mould, downy mildew and powdery mildew) using a citizen science approach in Hungary, a traditional wine- and grape-producing country. The present study has revealed that some vineyards (e.g., inclination, row orientation) and variety features (e.g., bunch structure) were considered crucial. At the same time, other factors were found to be less relevant in the present complex comparison, suggesting that the role of these factors might be overemphasised in the literature. In conclusion, the susceptibility or tolerance of grapevines to pathogens appears to be an integrated effect of several factors and cannot be assigned to a single characteristic. The global changes urge the revision of conventional agricultural traditions and deepen our knowledge about the infection process and pathogen-host-environment interactions.

Postharvest application of natamycin to control gray mold in table grapes

Gray mold caused by Botrytis cinerea is the most economically important postharvest disease of table grapes in California. In this study, the efficacy of the new biofungicide natamycin in controlling gray mold and its effects on fruit quality of table grapes were evaluated on two varieties. Four rates of natamycin treatment, ‘full’ (a.i. 920 mg L−1), ‘half’ (a.i. 460 mg L−1), ‘quarter’ (a. i. 230 mg L−1), and ‘eighth’ (a.i. 115 mg L−1) of the label rate, administered through dipping or spraying, were tested on grape berries that were with or without inoculation with B. cinerea prior to the treatment at 20 °C for 4 d or at 1 °C for 4 w. Natamycin significantly reduced percentage and severity of gray mold on the grape berries inoculated with B. cinerea, regardless of application methods, grape varieties, and incubation temperatures. Natamycin at all four rates also significantly reduced the percentage of grape berries showing fruit rots resulting from naturally occurring infections after the storage at 1 °C for 4 w, in comparison with the non-treated control, and remained effective when the fruit were evaluated after four-week cold storage followed by additional two days at 20 °C. Natamycin, however, had little effect on the spread of gray mold (nesting) within the grape cluster via fruit contact by mycelium of B. cinerea. Natamycin did not have any negative effects on the fruit quality parameters such as the appearance of grape rachis, firmness of berries, soluble solids, and titratable acidity. Our findings indicated that natamycin can be an effective postharvest treatment for managing gray mold on table grapes during storage.

Long amplicon nanopore sequencing of Botrytis cinerea and other fungal species present in infected grapevine leaf samples.

Botrytis cinerea is a well-known plant pathogen responsible for grey mould disease infecting more than 500 plant species. It is listed as the second most important plant pathogen scientifically and economically. Its impact is particularly severe in grapes since it affects both the yield of grape berries and the quality of wines. While various methods for detecting B. cinerea have been investigated, the application of Oxford Nanopore Technology (ONT) for complete ribosomal operon sequencing, which has proven effective in human and animal fungal research and diagnostics, has not yet been explored in grapevine (Vitis vinifera) disease research. In this study, we sequenced complete ribosomal operons (∼5.5 kb amplicons), which encompass the 18S, ITS1, 5.8S, ITS2, and 28S regions, from both pure cultures of B. cinerea and infected grapevine leaf samples. Minimap2, a sequence alignment tool integrated into the EPI2ME software, served as a taxonomy classifier, utilizing the custom reference database FRODO. The results demonstrate that B. cinerea was detectable when this pathogen was not the dominant fungal species in leaf samples. Additionally, the method facilitates host DNA-free sequencing and might have a good potential to distinguish other pathogenic and non-pathogenic fungal species hosted within grapevine's infected leaves, such as Alternaria alternata, Saccharomyces cerevisiae, Saccharomyces boulardii, Mucor racemosus, and Ascochyta rabie. The sequences were uploaded to the NCBI database. Long amplicon sequencing method has the capacity to be broadened to other susceptible crops and pathogens, as a valuable tool for early grey rot detection and mycobiome research. Future large-scale studies are needed to overcome challenges, such as comprehensive reference databases for complete fungal ribosomal operons for grape mycobiome studies.

From risk to reward: the strategic advantages of diversifying grape varietals

PurposeThe purpose of this study is to evaluate whether a diversification by grape varieties may help wine producers reduce uncertainty in quantity and quality variations due to increasingly erratic climate conditions.Design/methodology/approachThis study hand-collects granular quantity and quality data from wine harvest reports for vintages 2003 to 2017 for the Valais region in Switzerland. The data allows us to obtain detailed data on harvested kilograms/liters and Oechsle/Brix degrees. It is then merged with precise meteorological data over the same sample period. The authors use this data set to capture weather conditions and their impact on harvested quantities and quality. Finally, they build portfolios including different grape varieties to evaluate whether this reduces variations in quality and quantity over vintages.FindingsThe findings highlight that the weather varies relatively strongly over the sample period and that climate hazards such as hail, frost or ensuing vine diseases effectively occur. These strongly impact the harvested quantities but less the quality of the wine. The authors further show that planting different grape varieties allows for a significant reduction in the variation of harvested quantities over time and thus acts as a good solution against climate risk.Originality/valueThe effect of climate change on viticulture is becoming increasingly important and felt and bears real economic and social consequences. This study transposes portfolio diversification which is central to reducing risk in the finance industry, into the wine industry and shows that the same principle holds. The authors thus propose a novel idea on how to mitigate climate risk.

Rapid Grapevine Health Diagnosis Based on Digital Imaging and Deep Learning.

Deep learning plays a vital role in precise grapevine disease detection, yet practical applications for farmer assistance are scarce despite promising results. The objective of this research is to develop an intelligent approach, supported by user-friendly, open-source software named AI GrapeCare (Version 1, created by Osama Elsherbiny). This approach utilizes RGB imagery and hybrid deep networks for the detection and prevention of grapevine diseases. Exploring the optimal deep learning architecture involved combining convolutional neural networks (CNNs), long short-term memory (LSTM), deep neural networks (DNNs), and transfer learning networks (including VGG16, VGG19, ResNet50, and ResNet101V2). A gray level co-occurrence matrix (GLCM) was employed to measure the textural characteristics. The plant disease detection platform (PDD) created a dataset of real-life grape leaf images from vineyards to improve plant disease identification. A data augmentation technique was applied to address the issue of limited images. Subsequently, the augmented dataset was used to train the models and enhance their capability to accurately identify and classify plant diseases in real-world scenarios. The analyzed outcomes indicated that the combined CNNRGB-LSTMGLCM deep network, based on the VGG16 pretrained network and data augmentation, outperformed the separate deep network and nonaugmented version features. Its validation accuracy, classification precision, recall, and F-measure are all 96.6%, with a 93.4% intersection over union and a loss of 0.123. Furthermore, the software developed through the proposed approach holds great promise as a rapid tool for diagnosing grapevine diseases in less than one minute. The framework of the study shows potential for future expansion to include various types of trees. This capability can assist farmers in early detection of tree diseases, enabling them to implement preventive measures.

Searching new targets for the control of Black Rot: following the role of host factors modulating the infection process of Phyllosticta ampelicida.

Black Rot is a grapevine disease caused by the ascomycete Phyllosticta ampelicida. Neglected so far, this is developing into a pertinent problem in organic viticulture as resistant varieties are still lacking. Here, we follow cellular details of the infection process in the susceptible vinifera variety Müller-Thurgau and screen the ancestral European wild grapevine (V. vinifera sylvestris) for resistance to Black Rot. Using a standardized infection assay, we follow fungal development using LTSEM and quantify key stages on different hosts using fluorescence microscopy. There is considerable variation in susceptibility, which is associated with more rapid leaf maturation. Hyphal growth on different carbon sources shows a preference for pectins over starch, cellulose or xylans. In the resistant sylvestris genotypes Ketsch 16 and Ketsch 18 we find that neither spore attachment nor appressorium formation, but hyphal elongation is significantly inhibited as compared to Müller-Thurgau. Moreover, defence-related oxidative burst and accumulation of phenolic compounds is stimulated in the resistant genotypes. We arrive at a model, where more rapid maturation of the cell wall in these sylvestris genotypes sequesters pectins as major food source and thus block hyphal elongation. This paves the way for introgression of genetic factors responsible for cell wall maturation into V. vinifera to develop Black Rot-resistant varieties of grapevine.

Analysis of grape (Vitis Vinifera) diseases using neural networks

Grape (Vitis Vinifera) diseases cause a decrease in yield and product quality, and have an adverse effect on the growth, condition and resistance of bushes to frost. Some of the most common grape diseases can lead to poor berry quality and reduced yields, which can ultimately impact the income generated. To combat grape diseases, it is necessary to regularly treat plants with special preparations and monitor the condition of the plants throughout the growing season.

Recruiting grape‐isolated antagonistic yeasts for the sustainable bio‐management of Botrytis cinerea on grapes

AbstractBotrytis cinerea is the causative agent of grey mould disease in grapes, which was linked to significant postharvest losses. This study examined three grape‐isolated yeasts (Metschnikowia aff. fructicola, Metschnikowia pulcherrima, and Hansenispora uvarum) through in vitro and in vivo tests on detached grape berries against grey mould, as well as the elucidation of their possible mechanisms of action. The antifungal mechanism of action of yeasts was determined by the lytic enzyme activity, inhibition of spore germination, biofilm activity, iron depletion, diffusible metabolites, wound‐site colonisation, mycocin, and volatile organic compounds (VOCs) production. The highest in vitro efficacy (83.13%) was observed on M. aff. fructicola, followed by M. pulcherrima (82.10%) and H. uvarum (71.66%). Metschnikowia yeasts exhibited comparable enzyme activities, including protease, β‐1,3 glucanase, gelatinase chitinase, and cellulase, while H. uvarum had a poor enzymatic activity with chitinase and gelatinase. M. aff. fructicola showed relatively higher iron depletion activity than M. pulcherrima, while M. pulcherrima outperformed via diffusible metabolites. All yeast cultures significantly reduced spore germination by at least 86%. Overall, M. aff. fructicola exhibited the highest biocontrol activity with its iron depletion, inhibition of conidial germination, biofilm formation, VOCs, and well colonisation on grape berries. M. aff. fructicola 1‐UDM outperformed all other yeasts by significantly reducing disease incidence and lesion diameter values (93.4% and 94.3%, respectively). Remarkably, H. uvarum VOCs demonstrated potential as a biofumigant for suppressing grey mould. All yeasts are well adapted to their ecological niche to bio‐protect grapes from grey mould disease.

Molecular identification and antimicrobial potential of endophytic fungi against some grapevine pathogens.

Endophytic fungi are microorganisms that, exhibiting within the plant tissues without causing any apparent harm to the host, establish a symbiotic relationship with plants. Host plants provide endophytic fungi with essential nutrients and a protected environment. In exchange, the fungi can enhance the plant's ability to acquire nutrients. They can also play a crucial role in increasing the host plant's tolerance to various abiotic and biotic stresses. Endophytic fungi can produce a wide range of bioactive compounds, some similar to those found in the host plant. In Iraq's Duhok province of the Kurdistan region, the plant species Vitis vinifera has been explored as a habitat for diverse endophytic microorganisms across various ecological environments. During the period from 2021 to 2022, a total of 600 samples were collected from four distinct locations: Bagera, Besfke, Barebhar, and Atrush. From these samples, twelve endophytic fungal species were isolated, including Aspergillus flavipes, Botryosphaeria dothidea, Fusarium oxysporum, Fusarium ruscicol, Fusarium venenatum, Chaetomium globosum, Clonostachys rosea, Mucor racemosus, Penicillium glabrum, Aspergillus terreus, Aspergillus nidulans, and Aspergillus niger, Alternaria alternata, Paecilomyces maximus, Curvularia buchloes. These fungi were introduced for their potential as biocontrol agents against grapevine trunk diseases and grape rotting fungi, which pose significant risks to grapevine health and productivity. Penicilium radiatolobatum, Botrysphaeria dothidea, Fusarium ruscicola, Fusarium venenatum, and Paecilomyces maximus represented the first record as endophytes on grapevine in Iraq. Based on ITS and SSU sequencing, molecular identification confirmed these fungi's presence with sequence identities ranging from 99% to 100%. Phylogenetic analysis revealed that these endophytes could be categorized into five main clusters (A, B, C, D, and E), showing high intra-group similarity. Utilizing the Dual Culture method, the endophyte Paecilomyces maximus demonstrated a 70.83% inhibition rate against Ilyonectria destructans. In the Food Poisoning method, A. flavipes and P. maximus emerged as the most effective inhibitors of Ilyonectria destructans, whereas A. terreus, M. racemosus, and P. maximus achieved complete inhibition (100%) of Botrytis cinerea. Additionally, M. racemosus was identified as the most effective biocontrol agent against Neoscytalidium dimidiatum. In conclusion, the study emphasizes the potential of endophytic fungi from Vitis vinifera as effective biocontrol agents against grapevine diseases, highlighting their role in sustainable vineyard management. These findings lead to further exploration and implementation of these fungi-inserted pest management strategies.

Acquisition and transmission of Grapevine fanleaf virus (GFLV) by Xiphinema index and Xiphinema italiae (Longidoridae).

Grapevine fanleaf virus (GFLV) is one of the most severe virus diseases of grapevines, causing fanleaf degeneration that is transmitted by Xiphinema index. This paper aims to isolate Xiphinema species from Tunisian vineyard soil samples and assess their ability to acquire and transmit GFLV under natural and controlled conditions. Based on morphological and morphometric analyses, Tunisian dagger nematodes were identified as X. index and Xiphinema italiae. These results were confirmed with molecular identification tools using species-specific polymerase chain reaction primers. The total RNA of GFLV was extracted from specimens of Xiphinema and amplified based on real-time polymerase chain reaction using virus-specific primers. Our results showed that X. index could acquire and transmit the viral particles of GFLV. This nepovirus was not detected in X. italiae, under natural conditions; however, under controlled conditions, this nematode was able to successfully acquire and transmit the viral particles of GFLV.

Vineyard vigilance: Harnessing deep learning for grapevine disease detection

According to the International Organization of Vine and Wine, an impressive 77.8 million tons of grapes are cultivated globally each year. Grapes, long a cornerstone of both diets and agriculture worldwide, are increasingly threatened by diseases such as black rot, Esca, and leaf blight. In modern grapevine orchards, the prevalent method of disease detection relies on human observation, a process that often leads to delays in identifying these afflictions. This inefficiency typically results in diminished crop yields and inferior fruit quality. Automating grapevine leaf disease detection using machine learning will lead to sustainable farming. Deep learning, a branch of machine learning, is well-suited for learning from image data. In this paper, we deployed Convolutional Neural Networks (CNN), a type of deep learning model, to accurately classify healthy and disease-affected grapevine leaf images. We developed a baseline CNN model along with several transfer learning CNN models, namely DenseNet121, EfficientNetB7, MobileNetV2, ResNet50, and VGG16. We hypothesized that ResNet50 would be the best-performing model as it allows deep networks to train with high accuracy, but EfficientNetB7 turned out to be the most accurate model after experimentation. We created a max-voting ensemble with the three most accurate CNN models from the set, and it delivered better results than any individual CNN model. Our max-voting ensemble was then deployed on the web. Consequently, grapevine farmers and other users can detect the three common grapevine leaf diseases – black rot, Esca, and leaf blight, as well as healthy leaves by uploading their own images from the orchard.