IVES Conference Series

Effective control with fungicides is essential to protect grapevine from downy mildew, a devastating disease caused by the oomycete Plasmopara viticola. Managing this disease faces challenges in maintaining fungicide efficacy as the number of modes of action decreases and the risk of fungicide resistance increases. Long-term measures should address strains resistant to multiple modes of action, that can be selected by the repeated use of single-site fungicides. For these reasons, a precision management of the disease, that considers the selection of the best fungicide schedule according to the sensitivity profile of the pathogen population, is needed.

Downy mildew, caused by Plasmopara viticola, is the most economically impactful disease affecting grapevines. This polycyclic pathogen triggers both primary and secondary infection cycles, resulting in significant yield losses when effective disease control measures are lacking. Over the winter, the pathogen survives by forming resting structures, the oospores, derived from sexual reproduction, which produce the inoculum for primary infections. To optimize grapevine downy mildew control and obtain the desired levels of production while minimizing chemical inputs, it is crucial to optimize the timeframe for fungicide application. Disease forecasting models are useful to identify the infection risk.

The mapping of spatial variability in vineyards offers the potential to implement zonal management strategies with the aim to optimize economic benefits and increase sustainability by managing natural resources, such as water used for irrigation, more optimally. This study characterized the (natural) variability in plant water status in a commercial Cabernet Sauvignon block, using remote sensing techniques, and identified the impact of this variability on the yield, and juice and wine composition. From the field data collected over two growing seasons, we demonstrated that remote sensing techniques are a practical and powerful tool for mapping spatial variability within vineyard blocks.

Modern grapevine breeding programs have overcome many challenges using genomic selection, which has allowed breeders to make targeted selections at earlier stages in the breeding process. However, the cost of genetic testing may present a burden for some programs, and markers often struggle to accurately predict quantitative traits. Recent advances in high throughput, high-dimensional data have provoked investigation into the use of high-dimensional phenomics as a low-cost addition to the grape breeder’s toolkit that may offer advantages in predicting quantitative traits. High-dimensional secondary trait (HDST) data has been employed in annual crops for prediction of agriculturally important traits such as yield.

Flavescence dorée (FD) stands out as a significant grapevine disease with severe implications for vineyards. The American grapevine leafhopper (Scaphoideus titanus) serves as the primary vector, transmitting the pathogen that causes yield losses and elevated costs linked to uprooting and replanting. Another potential vector of FD is the mosaic leafhopper, Orientus ishidae, commonly found in agroecosystems. The current monitoring approach involves periodic human identification of chromotropic traps, a labor-intensive and time-consuming process.

Wine grape production is increasingly vulnerable to freeze damage due to warming climates, milder winters, and unpredictable late spring frosts. Traditional methods for assessing frost damage in grapevines which combine fieldwork and meteorological data, are expensive, time-consuming, and labor-intensive. Remote sensing could offer a rapid, inexpensive way to detect frost damage at a regional scale. Remote sensing approaches were used to assess freeze damage in grapevines by evaluating satellite-derived vegetation indices (VIs) to understand the severity and spatial distribution of damage in several New York vineyards immediately after a frost event (May 17th-18th, 2023). PlanetScope 3m satellite images acquired before and after the freeze were used to map damage and measure changes in VIs for vineyards in the Finger Lakes region.

Among the items preserved in gene banks, the old standard and autochthonous varieties represent an increasing value, since these varieties may have properties to make their cultivation more effective under changing climatic conditions. The increasingly extreme weather is a huge challenge for the viticulture. Collectional varieties can also play important role in protection against pests and pathogens. A genebank ensures not only the preservation of rare varieties, but also gives the opportunity for more knowledge and research of these varieties.

Recent developments in satellite technology have yielded a substantial volume of data, providing a foundation for various machine learning approaches. These applications, utilizing extensive datasets, offer valuable insights into Earth’s conditions. Examples include climate change analysis, risk and damage assessment, water quality evaluation, and crop monitoring. Our study focuses on exploiting satellite thermal and multispectral imaging, and vegetation indexes, such as NDVI, in conjunction with ground truth information about soil type, land usage (forest, urban, crop cultivation), and irrigation water sources in the Trentino region in North-East of Italy.

There is currently a real need to improve and speed-up phenotyping in experimental set-ups to increase the number of modalities studied. Accurate information acquisition on plant status with high-throughput capacity is the main appeal of on-board systems.
A proximal sensing camera for a proxy of winter pruning weight was tested. We estimated the shoot volume of the vine by image analysis using algorithms that integrate the local shoot section area estimate along the shoot skeleton obtained by a morphological distance transform.
The study was carried out on the GreffAdapt experimental vineyard in Guyot simple training and a canopy management using vertical trellising. The planting density is 6250 vines/ha with a row spacing of 1.6×1m. Five scions grafted onto 55 rootstocks are present and the combination rootstock×scion is different every five plants.