terclim by ICS banner
IVES 9 IVES Conference Series 9 Hyperspectral imaging and machine learning for monitoring grapevine physiology

Hyperspectral imaging and machine learning for monitoring grapevine physiology

Abstract

Rootstocks are gaining importance in viticulture as a strategy to combat abiotic challenges, as well as enhancing scion physiology and attributes. Therefore, understanding how the rootstock affects photosynthesis is insightful for genetic improvement of either genotype in the grafted grapevines. Photosynthetic parameters such as maximum rate of carboxylation of RuBP (Vcmax) and the maximum rate of electron transport driving RuBP regeneration (Jmax) have been identified as ideal targets for breeding and genetic studies. However, techniques used to directly measure these photosynthetic parameters are limited to the single leaf level and are time-consuming measurements. Hyperspectral remote sensing uses the optical properties of the entire vine to predict photosynthetic capacity at the canopy level. In this study, estimates of Vcmax and Jmax were assessed, in six different rootstocks with a common scion, using direct measurements and canopy reflectance obtained with hyperspectral wavelengths (400 to 1000 nm). Using artificial intelligence-based modeling, prediction models were developed for Marquette on the six different rootstock genotypes. Results for direct and indirect measures indicate that each rootstock promotes differences in scion Vcmax and Jmaxprofiles across the season. Application of machine learning and neural networks of spectral data provided good predictions of both photosynthetic parameters. 

DOI:

Publication date: June 14, 2024

Issue: Open GPB 2024

Type: Poster

Authors

Prakriti Sharma1, Anne Fennell1*

1 South Dakota State University, Brookings SD, USA

Contact the author*

Keywords

Hyperspectral, photosynthesis, neural networks, rootstock

Tags

IVES Conference Series | Open GPB | Open GPB 2024

Citation

Related articles…

South Africa’s top 10 Sauvignon blanc wines. How do the chemical and sensory profiles compare?

FNB Top 10 Sauvignon Blanc competition, presented by the Sauvignon Blanc Interest Group of South Africa and sponsored by First National Bank, is the country’s foremost platform for producers of this cultivar to showcase and benchmark their wines. Wines entered in the competition originated from all over the winegrowing regions of the country and the winning wines showed good representation of quality South African Sauvignon blanc wines. The ten selected wines were subjected to various chemical analyses including volatile thiol and methoxypyrazine determination, while the sensory profile of each wine was determined using projective mapping.

Haplotype-Resolved genome assembly of the Microvine

Developing a tractable genetic engineering and gene editing system is an essential tool for grapevine. We initiated a plant transformation and biotechnology program at Oregon State University using the grape microvine system (V. vinifera) in 2018 to interrogate gene-to-trait relationships using traditional genetic engineering and gene editing. The microvine model is also used for nanomaterial-assisted RNP, DNA, and RNA delivery. Most reference genomes and annotations for grapevine are collapsed assemblies of homologous chromosomes and do not represent the specific microvine cultivar ‘043023V004’ under study at our institution.

Grapevine yield estimation in a context of climate change: the GraY model

Grapevine yield is a key indicator to assess the impacts of climate change and the relevance of adaptation strategies in a vineyard landscape. At this scale, a yield model should use a number of parameters and input data in relation to the information available and be able to reproduce vineyard management decisions (e.g. soil and canopy management, irrigation). In this study, we used data from six experimental sites in Southern France (cv. Syrah) to calibrate a model of grapevine yield limited by water constraint (GraY). Each yield component (bud fertility, number of berries per bunch, berry weight) was calculated as a function of the soil water availability simulated by the WaLIS water balance model at critical phenological phases. The model was then evaluated in 10 grapegrowers’ plots, covering a diversity of biophysical and technical contexts (soil type, canopy size, irrigation, cover crop). We identified three critical periods for yield formation: after flowering on the previous year for the number of bunches and berries, around pre-veraison and post-veraison of the same year for mean berry weight. Yields were simulated with a model efficiency (EF) of 0.62 (NRMSE = 0.28). Bud fertility and number of berries per bunch were more accurately simulated (EF = 0.90 and 0.77, NRMSE = 0.06 and 0.10, respectively) than berry weight (EF = -0.31, NRMSE = 0.17). Model efficiency on the on-farm plots reached 0.71 (NRMSE = 0.37) simulating yields from 1 to 8 kg/plant. The GraY model is an original model estimating grapevine yield evolution on the basis of water availability under future climatic conditions.  It allows to evaluate the effects of various adaptation levers such as planting density, cover crop management, fruit/leaf ratio, shading and irrigation, in various production contexts.

Impact of winemaking practises on the formation of pinking

The pinking is a phenomenon that can occur in white wine produced with white grape causing the color change from yellow to red-salmon hue. Even if its appearance is highly variable and dependent to the vintage, the wines from certain grape varieties, such as Sauvignon blanc, Chardonnay, Riesling and Trebbiano di Lugana, have been identified to be more susceptible to the pinking.

REDWINE project: use of Chlorella vulgaris to prevent biotic and abiotic stress in Palmela’s region, Portugal, vineyards

The new EU Green Deal aims to achieve GHG emissions reduction by at least 55% by 2030 and a climate neutral EU economy by 2050.
REDWine concept will be realized through the establishment of an integrated Living Lab demonstrating the viability of the system at TRL 7. The Living Lab will be able to utilize 2 ton of fermentation off-gas/year (90% of total CO2 produced in the fermenter) and 80 m3 of liquid effluent (100% of the liquid effluent generated during fermenter washing) to produce 1 ton (dry weight) of Chlorella biomass/year. This biomass will be processed under a downstream extraction process to obtain added-value extracts and applied in food, cosmetic and agricultural end-products and to generate a new EcoWine. REDWine will focus on the recovery of off-gas from a 20.000L fermenter of red wine production existing in Adega Cooperativa de Palmela (ACP, located in Palmela, Portugal).