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.

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…

Exogenous dsRNA applications to identify novel candidate susceptibility genes to downy mildew

One of the major threats to viticulture is represented by fungal pathogens. Plasmopara viticola, an oomycete causing grapevine downy mildew, is one of the principal causes of grape production losses. The most efficient management strategies are represented by a combination of agronomical practices, fungicides’ applications, and use of resistant varieties. Plant resistance is conferred by the presence of resistance (R) genes. Opposed to them, susceptibility (S) genes are encoded by plants and exploited by pathogens to promote infection. Loss or mutation of S genes can limit the ability of pathogens to infect the host. By exploiting post-transcriptional gene silencing, known as RNA intereference (RNAi), it is possible to knock-down the expression of S genes, promoting plant resistance.

Sviluppo di una metodologia di tracciabilità e definizione dell’impronta petrochimica in suoli e vini della Sicilia occidentale nella piana di Marsala (TP)

I risultati delle ricerche condotte in un vigneto sperimentale di Marsala (TP), scelto per omogeneità di fattori bio-agronomici (età, tecniche colturali, potenzialità vegetativa e produttiva)

Effect of supplementation with inactive yeast during alcoholic fermentation in base wine for sparkling

INTRODUCTION: Foam stability of sparkling wines is significantly favored by the presence of surface active agents such as proteins and polysaccharides [1]. For that reason, the renowned sparkling wines are aged after the second fermentation in contact with the lees for several months (even years). Thereby wines are enriched in these macromolecules due to yeast autolysis. Since this practice is slow and costly, winemakers are seeking for alternative procedures to increase their concentration in base wines. In that sense, the supplementation with inactive yeast during alcoholic fermentation has been proposed [2]. The aim of this study was to determine whether this new strategy is really useful for enriching base wines in macromolecules and for improving foam properties of the base wines.

METHYL SALICYLATE, A COMPOUND INVOLVED IN BORDEAUX RED WINES PRODUCED WITHOUT SULFITES ADDITION

Sulfur dioxide (SO₂) is the most commonly used additive during winemaking to protect wine from oxidation and from microorganisms. Thus, since the 18th century, SO₂ was almost systematically present in wines. Recently, wines produced without any addition of SO₂ during all the winemaking process including bottling became more and more popular for consumers. A recent study dedicated to sensory characterization of Bordeaux red wines produced without added SO₂, revealed that such wines were perceived differently from similar wines produced with using SO₂ and were characterized by specific fruity aromas and coolness1,2.

NIR based sensometric approach for consumer preference evaluation

Climate change has had a global impact on grape production, and as a result, developing table grape varieties that can withstand climate-related threats has become a significant goal. However, it is equally important to ensure that these new grape varieties meet the preferences of consumers. To achieve this goal, a procedure has been developed that combines sensory analysis with spectroscopic data collected in the NIR region. Each sample was analyzed using both traditional analytical techniques and non-destructive NIR spectroscopy.