Factors Affecting the Vineyard Populational Diversity of Plasmopara viticola

Deep semantic segmentation for the quantification of grape foliar diseases in the vineyard

Plant disease evaluation is crucial to pathogen management and plant breeding. Human field scouting has been widely used to monitor disease progress and provide qualitative and quantitative evaluation, which is costly, laborious, subjective, and often imprecise. To improve disease evaluation accuracy, throughput, and objectiveness, an image-based approach with a deep learning-based analysis pipeline was developed to calculate infection severity of grape foliar diseases. The image-based approach used a ground imaging system for field data acquisition, consisting of a custom stereo camera with strobe light for consistent illumination and real time kinematic (RTK) GPS for accurate localization. The deep learning-based pipeline used the hierarchical multiscale attention semantic segmentation (HMASS) model for disease infection segmentation, color filtering for grapevine canopy segmentation, and depth and location information for effective region masking. The resultant infection, canopy, and effective region masks were used to calculate the severity rate of disease infections in an image sequence collected in a given unit (e.g., grapevine panel). Fungicide trials for grape downy mildew (DM) and powdery mildew (PM) were used as case studies to evaluate the developed approach and pipeline. Experimental results showed that the HMASS model achieved acceptable to good segmentation accuracy of DM (mIoU > 0.84) and PM (mIoU > 0.74) infections in testing images, demonstrating the model capability for symptomatic disease segmentation. With the consistent image quality and multimodal metadata provided by the imaging system, the color filter and overlapping region removal could accurately and reliably segment grapevine canopies and identify repeatedly imaged regions between consecutive image frames, leading to critical information for infection severity calculation. Image-derived severity rates were highly correlated (r > 0.95) with human-assessed values, and had comparable statistical power in differentiating fungicide treatment efficacy in both case studies. Therefore, the developed approach and pipeline can be used as an effective and efficient tool to quantify the severity of foliar disease infections, enabling objective, high-throughput disease evaluation for fungicide trial evaluation, genetic mapping, and breeding programs.

Phenotyping for QTL identification: A case study of resistance to Plasmopara viticola and Erysiphe necator in grapevine

Vitis vinifera is the most widely cultivated grapevine species. It is highly susceptible to Plasmopara viticola and Erysiphe necator, the causal agents of downy mildew (DM) and powdery mildew (PM), respectively. Current strategies to control DM and PM mainly rely on agrochemical applications that are potentially harmful to humans and the environment. Breeding for resistance to DM and PM in wine grape cultivars by introgressing resistance loci from wild Vitis spp. is a complementary and more sustainable solution to manage these two diseases. During the last two decades, 33 loci of resistance to P. viticola (Rpv) and 15 loci of resistance to E. necator (Ren and Run) have been identified. Phenotyping is salient for QTL characterization and understanding the genetic basis of resistant traits. However, phenotyping remains a major bottleneck for research on Rpv and Ren/Run loci and disease resistance evaluation. A thorough analysis of the literature on phenotyping methods used for DM and PM resistance evaluation highlighted phenotyping performed in the vineyard, greenhouse or laboratory with major sources of variation, such as environmental conditions, plant material (organ physiology and age), pathogen inoculum (genetic and origin), pathogen inoculation (natural or controlled), and disease assessment method (date, frequency, and method of scoring). All these factors affect resistance assessment and the quality of phenotyping data. We argue that the use of new technologies for disease symptom assessment, and the production and adoption of standardized experimental guidelines should enhance the accuracy and reliability of phenotyping data. This should contribute to a better replicability of resistance evaluation outputs, facilitate QTL identification, and contribute to streamline disease resistance breeding programs.

The Study of the Germination Dynamics of Plasmopara viticola Oospores Highlights the Presence of Phenotypic Synchrony With the Host

The plant disease onset is a complex event that occurs when the pathogen and the host encounter in a favorable environment. While the plant–pathogen interaction has been much investigated, little attention has been given to the phenological synchrony of the event, especially when both plant and pathogen overwinter, as in the case of grapevines and the downy mildew agent, the oomycete Plasmopara viticola. Oospores allow this obligate parasite to survive grapevine dormancy and, germinating, produce inoculum for primary infections. During overwintering, environmental factors influence the potential oospore germination. This study aimed at investigating the existence of synchrony between the pathogen and the host by identifying and quantifying the most important factors determining oospore maturation and germination and the relationship existing with grapevine phenology. Generalized linear models (GLM and GLMM) were used to analyze the germination dynamics of the oospores overwintered in controlled and field conditions and incubated in isothermal conditions, and oospore viability tests were carried out at different time points. Results showed that the most indicative parameter to describe the germination dynamics is the time spent by the oospores from the start of overwintering. The oospores overwintered in field showed phenological traits related to grapevine phenology not observed in controlled conditions. In particular, they completed the maturation period by the end of grapevine dormancy and germinated more rapidly at plant sprouting, when grapevine reaches susceptibility. Overall, the oospores proved to be able to modulate their behavior in close relationship with grapevine, showing a great adaptation to the host’s phenology.

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).

Biotechnological Approaches: Gene Overexpression, Gene Silencing, and Genome Editing to Control Fungal and Oomycete Diseases in Grapevine

Downy mildew, powdery mildew, and grey mold are some of the phytopathological diseases causing economic losses in agricultural crops, including grapevine, worldwide. In the current scenario of increasing global warming, in which the massive use of agrochemicals should be limited, the management of fungal disease has become a challenge. The knowledge acquired on candidate resistant (R) genes having an active role in plant defense mechanisms has allowed numerous breeding programs to integrate these traits into selected cultivars, even though with some limits in the conservation of the proper qualitative characteristics of the original clones. Given their gene-specific mode of action, biotechnological techniques come to the aid of breeders, allowing them to generate simple and fast modifications in the host, without introducing other undesired genes. The availability of efficient gene transfer procedures in grapevine genotypes provide valid tools that support the application of new breeding techniques (NBTs). The expertise built up over the years has allowed the optimization of these techniques to overexpress genes that directly or indirectly limit fungal and oomycetes pathogens growth or silence plant susceptibility genes. Furthermore, the downregulation of pathogen genes which act as virulence effectors by exploiting the RNA interference mechanism, represents another biotechnological tool that increases plant defense. In this review, we summarize the most recent biotechnological strategies optimized and applied on Vitis species, aimed at reducing their susceptibility to the most harmful fungal and oomycetes diseases. The best strategy for combating pathogenic organisms is to exploit a holistic approach that fully integrates all these available tools.