Can GAIA gauge fruit quality?
Yes, to a degree
GAIA can distinguish areas of good quality from areas of poor quality, based on satellite imagery alone. This can be used to inform split-picking schedules, and other critical decisions.
Over time, as GAIA continually learns and becomes smarter, we expect her to offer more predictive insight into the grade of fruit for specific areas.
Grape – and subsequently wine – quality is a complex function of a number of grape attributes, including sugar content, acidity, anthocyanin concentration and water content. These biochemical factors can be quantified through handheld spectrophotometers, however quality is frequently assessed through subjective expert tastings. Fruit quality directly impacts the value of the wine. The accumulation of sugars and flavour compounds is predominately light limited and so is directly related to the amount of shading experienced by the fruit, and hence correlates with measures of vine canopy coverage and density. Grape quality is also impacted by management practices (inputs such as fertilizers and other chemicals, vegetation cropping and canopy management, etc.), weather patterns and microclimates, soil characteristics, terrain, and any factors which alter the physiological characteristics of the vine. A single vineyard block can show significant variation in grape quality, with intra-block variation of more than 10% in quality parameters observed (e.g. Baumé and phenolics) and fruit spanning several quality grades. “Uplift” is the process of increasing the quality (grade) of grapes to increase the supply of higher retail price wines (e.g. increasing from C & D grade fruit to A & B grade).
The character and quality assessment of grapes is correlated with the Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI or plant cell density PCD). Higher quality fruit originates from areas of lower values of the vegetation indices (VIs). The qualitative relationship between VIs and quality can be used to delineate relative quality boundaries (ie. high, moderate, low) for each vineyard block. These boundaries would be expected to be relatively temporally stable and can be utilised for targeted harvesting “split picking” across seasons.
The relationship between grape composition and vine physiology is complex. Canopy coverage and density can be remotely sensed, however manipulation of the vine canopy (e.g. vine balance) has no consistent significant impact on fruit quality parameters (anthocyanin, phenolic or tannin concentration) and is not a reliable mechanism for “uplift”. Therefore there is unlikely to be a consistent quantitative relationship between singular measures of vine vigour (such as VIs) and grape composition and wine quality. Other attributes of the vineyard site (terroir) are more significant in determining fruit quality, and these may be captured through multispectral remote sensing. A more complex multivariate measure of: the vegetation canopy, combining measures of vigor and biomass (e.g. leaf area); the leaf biochemical composition (e.g. water, chlorophyll, protein and lignin content); and the underlying soil or covercrop background can be derived from multispectral VNIR and SWIR satellite imagery. Such a measure has the capability of being able to quantitatively discriminate crops of different grape quality by providing sensitivity to both the supply of energy and organic carbon to the vine, and the various other aspects of terroir that influence grape quality. Once established, a quantitative relationship could be used to systematically inform “uplift” practices.
