Oral – Advances in precision viticulture and phenotyping

Increasing water scarcity and unpredictable rainfall patterns necessitate efficient water management in grape production. This study proposes a novel approach for monitoring grapevine water status in a commercial vertically-shoot-positioned Vitis vinifera L. Tempranillo vineyard using non-invasive spectroscopy with a battery of different AI methods to assess vineyard water status, that could drive precise irrigation. A contactless, miniature NIR spectrometer (900-1900 nm) mounted on a moving vehicle (3 Km/h) was employed to collect spectral data from the vines' northeast side along six dates in season 2021.

Drought and heat stress will pose challenges for the future of viticulture and wine quality, as grapevine biological processes are pushed beyond their optimum conditions. Efforts are increasing to study and predict the effects of drought spells and heatwaves on grapevine physiology and resulting harvest quality. This calls for the development of adequate systems to induce and manipulate the required stress, especially in open field trials where conditions are more difficult to control. We present a semi-controlled system for studying drought and heat stress in grapevine in the field.

Elemental composition, measured as the concentrations of different elements present in a given tissue at a given time point, is a key indicator of vine health and development. While elemental composition and other high-throughput phenotyping approaches yield tremendous insight into the growth, physiology, and health of vines, costs and labor associated with repeated measures over time can be cost-prohibitive. Recent advances in handheld sensors that measure hyperspectral reflectance patterns of leaf tissue may serve as an affordable proxy for other types of phenotypic data, including elemental composition. Here, we ask if reflectance patterns of dried Chambourcin leaf tissue from an experimental grafting vineyard can predict the known elemental composition of those leaves.

Aerial thermography has emerged as a promising tool for water stress detection in grapevines, but there are still challenges associated with this technology, particularly concerning the methodology employed to extract reliable canopy temperature values. This consideration is relevant especially in vertically trained vineyards, due to the presence of multiple surfaces which are captured by drone thermal cameras with high-resolution. To test the technology and the data analysis required, a field study was conducted during the 2022-2023 season in a model vineyard with multiple scions-rootstock combinations trained on a vertical shoot-positioning (VSP) system. Additionally, three irrigation regimes were implemented to introduce variability in water stress levels.

The application of sensors in viticulture is a fast and efficient method to monitor grapevine vegetative, yield and quality parameters and determine their spatial intra-vineyard variability. Molecular analysis at the gene expression level can further contribute to the understanding of the observed variability by elucidating how pathways responsible for different grape quality traits behave in zones diverging for one or the other parameter. The intra-vineyard variability of a Cabernet Sauvignon vineyard was evaluated by a standard Normalized Difference Vegetation Index (NDVI) mapping approach, employing UAV platform, accompanied by detailed ground-truthing (e.g. vegetative, yield, and berry ripening compositional parameters) that was applied in 14 spots in the vineyard. Berries from different spots were additionally investigated by microarray gene expression analysis, performed at five time points from fruit set to full ripening.

Developing rootstocks adapted to environmental constraints constitutes a key lever for grapevine adaptation to climate change. In this context, Near Infrared Spectroscopy (NIRS) could be used as a high-throughput phenotyping technique to simplify the study of rootstocks in grafted situations. This study is an exploratory analysis to evaluate the potential of NIRS acquired on grafted tissues to reveal rootstock effects as well as the plasticity of combinations of scion/rootstock to better characterize these interactions.
Through the study of 25 combinations (5 scions times 5 rootstocks) in a dedicated experimental vineyard, we showed that NIRS obtained from grafted tissues capture rootstock and scion/rootstock interaction signals, up to 20% of the total variance at specific wavelengths.