Assessing wine grape quality parameters using plant traits derived from physical model inversion of hyperspectral imagery

Abstract Together with ensuring a stable yield, improving grape composition and aroma is the main goal of wine grape production management as it determines consumer acceptance and ultimately revenue. Understanding the triggers of the synthesis of aromatic components and finding methods to map their variability in the field can aid management practices during the season and planning selective harvest in views of maximizing benefit. Vegetation indices have been shown to track grape colour, sugar and acidity content but it has been demonstrated that aromatic components are the main drivers of the final palate of wine and are not correlated to sugar concentration. Leaf pigments such as chlorophyll, carotenoids and anthocyanins are involved in the metabolic pathways of aroma compounds in grapes. The physiological connections between grape aromatic components and primary and secondary photosynthetic pigments suggest that they could be used to detect processes related to aroma composition. This study investigates the links between grape quality parameters such as aromatic components and image-quantified spectral indices and photosynthetic plant traits derived by physical model inversion methods. Two sets of high-spatial resolution hyperspectral and thermal imagery were collected with an unmanned platform at veraison and harvest. The variability found in the field was partly but not fully explained by the thermal-based crop water stress index as an indicator of water stress (r2= 0.51–0.58, p-value