terclim by ICS banner
IVES 9 IVES Conference Series 9 Combination of NIR multispectral information acquired from a ground moving vehicle with AI methods to assess the vine water status in a Tempranillo (Vitis vinifera L.) commercial vineyard

Combination of NIR multispectral information acquired from a ground moving vehicle with AI methods to assess the vine water status in a Tempranillo (Vitis vinifera L.) commercial vineyard

Abstract

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.Grapevines were monitored at solar noon using stem water potential (Ψs) as reference parameter of plant water status. At each date, 36 measurements of Ψs were taken making a total of 396 data in the whole season. AI techniques, including linear regression, gaussian process regression (GPR) support vector machine (SVM), and neural networks, trained with Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) algorithms were implemented in MATLAB (using the Regression Learner and Natural Net Fitting apps) to analyze the spectral data and predict vine water status. The optimized GPR model achieved the best performance, with a determination coefficient (R2P) above 0.83 and a root mean squared error of prediction (RMSEP) of 0.112 MPa. However, several neural network models trained with the LM algorithm exhibited superior performance, with R2P values over 0.92 and RMSEP values of approximately 0.080 MPa. This study demonstrates the potential of non-invasive spectroscopy combined with AI methods for accurate prediction of grapevine water status, paving the way for precision irrigation in vineyards.

Download the abstract

DOI:

Publication date: June 14, 2024

Issue: Open GPB 2024

Type: Article

Authors

Fernando Rubio-Ordoyo1, María Paz Diago,1,2, Ignacio Barrio1,2, Juan Fernández-Novales1,2*

1 Department of Agriculture and Food Science. University of La Rioja. C/Madre de Dios 53. 26007. Logroño, (La Rioja) Spain
2 Institute of Sciences of Vine and Wine (CSIC, University of La Rioja, La Rioja Government) Finca La Grajera. Ctra. de Burgos Km 6. 26007. Logroño. (La Rioja). Spain

Contact the author*

Keywords

Vine water status, NIR spectrophotometer, Stem water potential, Gaussian Regression Process, Levenberg-Marquardt algorithm

Tags

IVES Conference Series | Open GPB | Open GPB 2024

Citation

Related articles…

Study of the volatile aroma profile of five Italian grape varieties submitted to controlled postharvest withering

Wines made with grapes submitted to postharvest dehydration are often referred to as “passito” or “straw wines.” This distinct style of winemaking consists of a process of water loss that allows the berries to undergo a mild water stress and senescence process [1].

Read More

Correlation between agronomic performance and resistance gene in PIWi varieties in the field

Today’s viticulture faces a considerable challenge dealing with fungal diseases and limitations on the use of plant protection products (PPP) have increased the pressure to find more sustainable alternatives. One strategy may be the development and cultivation of disease-resistant grapevine varieties (PIWI) that could maintain crop productivity and quality while reducing dependence on PPP. In this work a set of 9 PIWI varieties (5 white and 4 red) deploying genes for resistance to powdery and downy mildew were evaluated in two consecutive years in Valdegón, La Rioja, with Tempranillo and Viura as controls. The objective was to correlate agronomic performance and disease incidence with the presence of disease resistance genes in two different seasons: with (2023) and without disease pressure (2022).

Read More

Effects of the synergy between T. delbrueckii and S. cerevisiae in the winemaking of traditional cultivars from southeastern Italy

The combination of Torulaspora delbrueckii and Saccharomyces cerevisiae in co-inoculation and sequential inoculation in winemaking was investigated as an innovative strategy to increase the aromatic profile of wines like Verdeca and Nero di Troia wines, two traditional varieties from south-eastern Italy (Apulia Region).

Read More

Effet de l’ombrage respectif des ceps et des grappes de Muscat sur leurs teneurs en composés volatils libres et glycosyles et en précurseurs d’aromes carotenoïdiques

Le Muscat de Frontignan est bien connu pour ses fortes teneurs en composés terpéniques et par l’odeur florale et fruitée que ces composés confèrent aux vins qui en sont issus (1,2).

Read More

Utilisation de données historiques pour caractériser le millésime en cours

Cet article propose la formalisation d’un modèle paramétrique pour représenter l’accumulation des sucres dans les baies de raisin durant la maturation. Le test de ce modèle sur des jeux de données réels a permis de valider l’approche proposée. Une seconde partie est axée sur l’adaptation de la méthode pour permettre la simulation du comportement du millésime en cours dès les premiers relevés de maturité. Ce travail possède de multiples applications dans le domaine de l’aide à la décision.

Read More