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Modern and sustainable viticulture entails objective and fast monitoring of crucial variables for rational decision making. The development of new, non-invasive technologies in the last decade has enabled the acquisition of large amount of data from the vineyard, which need to be properly analysed to provide helpful information to viticulturists. In this context, data mining strategies may be applied to agricultural data, with the aim of yielding useful, reliable and objective information. This work presents the most recent applications of machine learning algorithms to grapevine plant phenotyping, specifically to variety discrimination, and assessment of plant water status. Support vector machine (SVM) and modified partial least squares (MPLS) models were built using NIR spectra acquired in the vineyard, on grapevine leaves, with a portable spectrophotometer working on the spectral range between 1600 to 2500 nm. Spectral measurements were acquired on the adaxial side of 200 individual leaves (20 leaves per cultivar) of ten (Vitis vinifera L.) varieties. Sequential minimal optimization (SMO) algorithm was used for the training of a SVM for varietal classification. The classifier’s performance for the 10 varieties surpassed the 94.9% mark. For water stress assessment, the predictive model based on MPLS using the reflectance spectra of four cultivars, and the first and second derivative, yielded a R2= 0.81 for stem water potential (ys), which is widely recognized as an integrative indicator of whole-vine water status, but destructive and very laborious. These results show the power of the combined use of data mining and non-invasive sensing for grapevine phenotyping and their usefulness for the wine industry.

<strong>Data mining and non-invasive proximal sensing for precision viticulture</strong>