Hyperspectral imaging and machine learning for monitoring grapevine physiology

The study of anthocyanins in wines and grapes has been the subject of numerous research works over the years due to their important role in enology regarding their contribution to wine sensory properties.

It is fundamental for wineries to know the potential yield of their vineyards as soon as possible for future planning of winery logistics. As such, non-invasive image-based methods are being investigated for early yield prediction. Many of these techniques have limitations that make it difficult to implement for practical use commercially. The aim of this study was to assess whether yield can be estimated using images taken in-field with a smartphone at different phenological stages. The accuracy of the method for predicting bunch weight at different phenological stages was assessed for seven different varieties.

Chardonnay is the world’s most planted white grape variety and has met a great commercial success for decades.

Full understanding of grapevine responses to variable soil resources requires
assessing the grapevine root system. Grapevine root systems are expansive and examining deep roots (i.e., >40 cm)
is particularly important in conditions where grapevines increase reliance on deep soil resources, such as drought
or plant competition. Traditional methods of assessing roots rely on morphological traits associated specific
functions (e.g., root color, diameter, length), while recent methodological advances allow for estimating root
function more directly (e.g., omics). Yet, the potential of applying refined methods remains underexplored for roots
at deep depths.

Aim: The aim of this study was to quantify key berry sugar accumulation traits and characterize their plasticity in response to climate variation from data collected from different cultivars over seven years from an experimental vineyard.