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Software to open a zip file and Excel.-- The 3 datasets are available as .csv files, This dataset contain the raw data for the preparation of the aforementioned manuscript., Spanish Ministry of Economy and Competitiveness (MINECO). INNGRAPE Project (RTC-2014-3058-2)., Peer reviewed

The identification of different grapevine varieties, currently attended using visual ampelometry, DNA analysis and very recently, by hyperspectral analysis under laboratory conditions, is an issue of great importance in the wine industry. This work presents support vector machine and artificial neural network's modelling for grapevine varietal classification from in-field leaf spectroscopy. Modelling was attempted at two scales: site-specific and a global scale. Spectral measurements were obtained on the near-infrared (NIR) spectral range between 1600 to 2400 nm under field conditions in a non-destructive way using a portable spectrophotometer. For the site specific approach, spectra were collected from the adaxial side of 400 individual leaves of 20 grapevine (Vitis vinifera L.) varieties one week after veraison. For the global model, two additional sets of spectra were collected one week before harvest from two different vineyards in another vintage, each one consisting on 48 measurement from individual leaves of six varieties. Several combinations of spectra scatter correction and smoothing filtering were studied. For the training of the models, support vector machines and artificial neural networks were employed using the pre-processed spectra as input and the varieties as the classes of the models. The results from the pre-processing study showed that there was no influence whether using scatter correction or not. Also, a second-degree derivative with a window size of 5 Savitzky-Golay filtering yielded the highest outcomes. For the site-specific model, with 20 classes, the best results from the classifiers thrown an overall score of 87.25% of correctly classified samples. These results were compared under the same conditions with a model trained using partial least squares discriminant analysis, which showed a worse performance in every case. For the global model, a 6-class dataset involving samples from three different vineyards, two years and leaves monitored at post-veraison and harvest was also built up, reaching a 77.08% of correctly classified samples. The outcomes obtained demonstrate the capability of using a reliable method for fast, in-field, non-destructive grapevine varietal classification that could be very useful in viticulture and wine industry, either global or site-specific., This work has received funding from the Spanish Ministry of Economy and Competitiveness (MINECO) under the INNGRAPE project (RTC-2014-3058-2)., Peer Reviewed

Plant phenotyping is a very important topic in agriculture. In this context, data mining strategies may be applied to agricultural data retrieved with new non-invasive devices, with the aim of yielding useful, reliable and objective information. This work presents some applications of machine learning algorithms along with in-field acquired NIR spectral data for plant phenotyping in viticulture, specifically for grapevine variety discrimination and assessment of plant water status. Support vector machine (SVM), rotation forests and M5 trees models were built using NIR spectra acquired in the field directly on the adaxial side of grapevine leaves, with a non-invasive portable spectrophotometer working in the spectral range between 1600 and 2400 nm. The ν-SVM algorithm was used for the training of a model for varietal classification. The classifiers’ performance for the 10 varieties reached, for cross- and external validations, the 88.7% and 92.5% marks, respectively. For water stress assessment, the models developed using the absorbance spectra of six varieties yielded the same determination coefficient for both cross- and external validations (R = 0.84; RMSEs of 0.164 and 0.165 MPa, respectively). Furthermore, a variety-specific model trained only with samples of Tempranillo from two different vintages yielded R = 0.76 and RMSE of 0.16 MPa for cross-validation and R = 0.79, RMSE of 0.17 MPa for external validation. These results show the power of the combined use of data mining and non-invasive NIR sensing for in-field grapevine phenotyping and their usefulness for the wine industry and precision viticulture implementations., This work has received funding from the Spanish Ministry of Economy and Competitiveness (MINECO) under the INNGRAPEproject (RTC-2014-3058-2).
This article belongs to the Special Issue Selected Papers from the 2nd International Electronic Conference on Sensors and Applications., Peer Reviewed

Precise and rapid methods to assess plant water status are needed in agriculture. The goal of this work was to evaluate the capability of a new plant-based method based on proximal near-infrared (NIR) spectroscopy acquired on-the-go from a moving vehicle to quantify and discriminate different water regimes in a commercial vineyard. Proximal on-the-go NIR spectroscopy (1100–2100 nm) was acquired at solar noon on five days from veraison (onset of ripening) to harvest 2015 in a commercial Tempranillo vineyard. Spectral measurements were taken at ∼0.30 m from the canopy, on both canopy sides, from a vehicle moving at 5 km h−1. Measurements of midday stem water potential (Ψs) and leaf stomatal conductance (gs) were simultaneously acquired to be used as reference indicators of plant water status. Partial least squares (PLS) was used to build calibration, cross validation and predictive models for Ψs and gs. The determination coefficients of prediction (R2p) were above 0.86 for Ψs and above 0.66 for gs, while the root mean square errors of prediction (RMSEP) were less than 0.18 MPa and 93.7 mmol [H2O] m−2 s−1, respectively. PLS-Discriminant Analysis (PLS-DA) was applied to classify the data into three different water regimes, according to Ψs or gs. The average correctly classified percentage was greater than 72% for Ψs and gs. This discriminant capability, together with the large number of measurements that the on-the-go NIR spectroscopy can provide, enables the quantification and mapping of the variability of a vineyard water status and may help to define precise irrigation strategies in viticulture., his work has received funding from the Spanish Ministry of Economy and Competitiveness (MINECO) under the INNGRAPE project (RTC-2014-3058-2). The work leading to these results has received funding from the European Union under grant agreement n°610953 (VINEROBOT project). Salvador Gutiérrez would like to acknowledge the research founding FPI grant 299/2016 by Universidad de La Rioja, Gobierno de La Rioja. Maria P. Diago is funded by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO) with a Ramon y Cajal grant RYC-2015-18429., Peer reviewed

Assessing water status and optimizing irrigation is of utmost importance in most winegrowing countries, as the grapevine vegetative growth, yield, and grape quality can be impaired under certain water stress situations. Conventional plant-based methods for water status monitoring are either destructive or time and labor demanding, therefore unsuited to detect the spatial variation of moisten content within a vineyard plot. In this context, this work aims at the development and comprehensive validation of a novel, non-destructive methodology to assess the vineyard water status distribution using on-the-go, contactless, near infrared (NIR) spectroscopy. Likewise, plant water status prediction models were built and intensely validated using the stem water potential (ψs) as gold standard. Predictive models were developed making use of a vast number of measurements, acquired on 15 dates with diverse environmental conditions, at two different spatial scales, on both sides of vertical shoot positioned canopies, over two consecutive seasons. Different cross-validation strategies were also tested and compared. Predictive models built from east-acquired spectra yielded the best performance indicators in both seasons, with determination coefficient of prediction (R2P) ranging from 0.68 to 0.85, and sensitivity (expressed as prediction root mean square error) between 0.131 and 0.190 MPa, regardless the spatial scale. These predictive models were implemented to map the spatial variability of the vineyard water status at two different dates, and provided useful, practical information to help delineating specific irrigation schedules. The performance and the large amount of data that this on-the-go spectral solution provides, facilitates the exploitation of this non-destructive technology to monitor and map the vineyard water status variability with high spatial and temporal resolution, in the context of precision and sustainable viticulture., This work has received funding from the Spanish Ministry
of Economy and Competitiveness (MINECO) under the
INNGRAPE project (RTC-2014-3058-2). The work leading to
these results has received funding from the European Union
under grant agreement n◦
610953 (VINEROBOT project). SG
would like to acknowledge the research founding FPI grant
299/2016 by Universidad de La Rioja, Gobierno de La Rioja.
MD is funded by the Spanish Ministry of Economy, Industry
and Competitiveness (MINECO) with a Ramon y Cajal grant
RYC-2015-18429., Peer reviewed

[Background] Until now, the majority of methods employed to assess grapevine water status have been destructive, time‐intensive, costly and provide information of a limited number of samples, thus the ability of revealing within‐field water status variability is reduced. The goal of this work was to evaluate the capability of non‐invasive, portable near infrared (NIR) spectroscopy acquired in the field, to assess the grapevine water status in diverse varieties, grown under different environmental conditions, in a fast and reliable way. The research was conducted 2 weeks before harvest in 2012, in two commercial vineyards, planted with eight different varieties. Spectral measurements were acquired in the field on the adaxial and abaxial sides of 160 individual leaves (20 leaves per variety) using a commercially available handheld spectrophotometer (1600–2400 nm).
[Results] Principal component analysis (PCA) and modified partial least squares (MPLS) were used to interpret the spectra and to develop reliable prediction models for stem water potential (Ψs) (cross‐validation correlation coefficient (rcv) ranged from 0.77 to 0.93, and standard error of cross validation (SECV) ranged from 0.10 to 0.23), and leaf relative water content (RWC) (rcv ranged from 0.66 to 0.81, and SECV between 1.93 and 3.20). The performance differences between models built from abaxial and adaxial‐acquired spectra is also discussed.
[Conclusions] The capability of non‐invasive NIR spectroscopy to reliably assess the grapevine water status under field conditions was proved. This technique can be a suitable and promising tool to appraise within‐field variability of plant water status, helpful to define optimised irrigation strategies in the wine industry, This work has received funding from the Spanish Ministry of Economy and Competitiveness (MINECO) under the INNGRAPE project (RTC‐2014‐3058‐2). The authors wish to thank Vitis Navarra S.L. (Larraga, Navarra, Spain) for providing the vineyards to carry out this study, Peer reviewed

Must nitrogen composition plays a key role on wine quality, affecting the development of alcoholic fermentation and the formation of volatile compounds. In order to provide additional information about the optimal time of harvest, we studied grape amino acid evolution pattern in relation to the accumulation of soluble solids along ripening. Additionally, we evaluate the amino acid profile of Grenache (Vitis vinifera L.) grapevines. The results showed that the degree of berry maturity strongly influence the evolution patterns of all amino acids. Grenache behaved as an arginine accumulator variety. This amino acid was found to model the total amino acid concentration in Grenache musts. Generally, soluble solids maturity coincided with the nitrogen maturity at 25 ºBrix. Therefore, in Grenache grapevines, when amino acid composition is aimed to be maximized, the optimal grape harvest time, in terms of nitrogenous maturity, matches the timing at which berries reach their sugar maturity at 25 ºBrix., This work has received funding from the Spanish Ministry of Economy and Competitiveness (MINECO) under the INNGRAPE project (RTC-2014-3058-2). The work leading to these results has received funding from the European Union under grant agreement nº610953 (VINEROBOT project). The authors acknowledge Juan Antonio Blanco Hernáez for providing the vineyards to carry out this study. Special thanks to Fernando García and Diego Canalejo for their help in the sample preparation and the analysis of amino acids. Maria P. Diago is funded by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO) with a Ramón y Cajal grant RYC-2015-18429. Teresa Garde-Cerdán thanks MINECO for funding her Ramón y Cajal contract. Eva Pilar Pérez-Álvarez thanks the MINECO for her contract. Gastón Gutiérrez-Gamboa thanks for the financial support given by CONICYT through, BCH/Doctorado–72170532., Peer reviewed

The amino acid concentration assessment along grape ripening would provide valuable information regarding harvest scheduling, wine aroma potential and must nitrogen supplement addition. In this work the use of Visible (Vis) and near-infrared (NIR) spectroscopy to estimate the grape amino acid content along maturation on intact berries was investigated. Spectral data on two ranges (570–1000 and 1100–2100 nm) were acquired contactless from intact Grenache berries. A total of 22 free amino acids in 128 grape clusters were quantified by HPLC. Partial least squares was used to build calibration, cross validation and prediction models. The best performances (R2P ~ 0.60) were found for asparagine (SEP: 0.45 mg N/l), tyrosine (SEP: 0.33 mg N/l) and proline (SEP: 17.5 mg N/l) in the 570–1000 nm range, and for lysine (SEP: 0.44 mg N/l), tyrosine (SEP: 0.26 mg N/l), and proline (SEP: 15.54 mg N/l) in the 1100–2100 nm range. Remarkable models (R2P~0.90, SEP~1.60 ºBrix, and RPD~3.79) were built for total soluble solids in both spectral ranges. Contactless, non-destructive spectroscopy could be an alternative to provide information about grape amino acids composition., This work has received funding from the Spanish Ministry of Economy and Competitiveness (MINECO) under the INNGRAPE project (RTC-2014-3058-2). The work leading to these results has received funding from the European Union under grant agreement no 610953 (VINEROBOT project)., Peer reviewed