Selective harvest guided by drone maps: the case study of Fattoria di Cinciano in Chianti Classico

Introduction

In recent years, viticulture has been facing an increasingly complex challenge: producing high-quality wines efficiently and sustainably, in line with market demands, while at the same time reducing the subjective component in agronomic decisions. In this context, precision agriculture technologies represent a key tool for reading and managing the natural variability of the vineyard. Among the digital solutions currently available, the use of drone-based vigour maps makes it possible to turn agronomic information into field operations. This approach opens up new possibilities, even for a traditionally manual and subjective stage such as selective harvesting, making it possible to move from selection based on visual experience to a harvest guided by objective, spatialised data. The case study presented in this article shows how integrating remote sensing, vineyard zoning and differentiated harvest management can lead to the production of distinct wines, each an expression of the vineyard’s different growth and production conditions.

The Fattoria di Cinciano case

Within the Chianti Classico DOCG area lies the Cinciano estate. The estate’s vineyard covers 28 hectares and is dominated by the Sangiovese variety, which covers around 90% of the entire planted area. Although the plots are located in a fairly concentrated area, they are characterised by strong variability in soil and altitude. Indeed, soils with a strong silty component can be found at 150 m above sea level, moving to soil rich in galestro and clay in the central part, up to 360 m above sea level, where a good sandy component is also found.

From manual to automated selection

In the past, grapes for the production of Chianti Classico “Gran Selezione” were harvested through visual selection of the best bunches. It is clear that this method also carries variability due to the subjective assessment parameters of whoever carries out the harvest. To make the assessment objective, it is possible to correlate the vigour index, the production load and the quantitative-qualitative characteristics of the grapes. In the case study presented today, a DJI Mavic Multispectral drone was used for aerial surveys, in order to obtain RGB (true-colour) and multispectral images of the vineyard examined. Using photogrammetry software, it was possible to build an orthomosaic of the field, from which maps based on vegetation indices were extracted. Specifically, a map based on the NDVI index and the corresponding zoning map were created, considering 3 homogeneous areas (high, medium and low vigour). The evolution of the data, from raw data to map, is shown in Figure 1.

Fig.1: Image processing protocol: from RGB orthomosaic to NDVI map in order to generate homogeneous areas (green = high vigour; yellow = medium vigour; red = low vigour).

Zoning is essential for selecting the areas in which to sample grapes. Specifically, 9 sampling points were identified (Figure 2), i.e. 3 points for each homogeneous area shown in the figure. At each point, the following surveys were carried out:

1. 3D scan of the vine using iAgro;
2. weighing and counting of bunches per plant;
3. analysis of samples at an accredited laboratory.

Fig.2: Grape sampling based on zoning.

The iAgro app creates the digital twin of the plant or row section and measures the canopy’s main biometric parameters: canopy height, thickness and volume, the LAI (Leaf Area Index), TRV (Tree Row Volume) and LWA (Leaf Wall Area) indices. Based on this data, the optimal dose of crop protection mixture is also calculated. For each plant selected, the bunches were counted, weighed and subsequently analysed to assess a range of parameters: average berry weight, skin/juice ratio, sugar concentration, total acidity, pH, malic acid, readily assimilable nitrogen (RAN), anthocyanin extractability potential at pH 1, extractable anthocyanins at pH 3.2 and percentage of extractable anthocyanins, phenol index and percentage of tannins in the seeds.

Selective grape harvesting

To obtain the Chianti Classico “Gran Selezione”, the best bunches were harvested from the areas characterised by low vigour (in red). The best bunches are defined as loose-clustered bunches of medium and small size, with no mould, rot, unripe berries or heat-stroke burns. In a second pass, the remaining bunches in the red areas were also harvested. In the third pass, bunches from the high-vigour areas (in green) were harvested. Figure 3 shows the harvest layout used during the harvest from the tractor.

Fig.3: Harvest layout: in red, the medium-low vigour areas; in green, the high vigour areas.

It is important to note that the 3 batches harvested were used to produce 3 different wines. To avoid influencing the properties of the grapes, each vinification process was carried out in the same way: the same strain of selected yeasts, the same additions of activators, nutrients and enological additives.

Results: grapes and wines compared

Looking at the grape sampling data, it is clear that the vineyard’s different vigour zones produce grapes with very different characteristics (Figure 4). The high-vigour areas show a higher production load, with larger bunches and berries on average and a lower skin/juice ratio. These conditions are directly reflected in the composition of the grapes, leading to lower values of sugar and total acidity. Moving towards the medium-low vigour zones, production decreases but the concentration of the main quality parameters increases. Another notable aspect concerns malic acid, present in greater quantities in the more vigorous plants. This is mainly linked to the bunch’s microclimate: in high-vigour zones, the more developed canopy protects the berries from direct sunlight and high temperatures, slowing the degradation of malic acid compared with more exposed zones. Finally, a higher concentration of phenolic compounds is recorded in low-vigour zones.

Fig.4: Maps of the main quantitative parameters (yield per hectare, number of bunches, bunch yield) and qualitative parameters (sugars, total acidity, anthocyanins).

As for the wines obtained from the 3 batches harvested (Table 1), the first clear difference concerns grape production per hectare, significantly higher in the high-vigour zone, while it is similar in the other two batches. However, wine yield changes significantly, following a clear scale: 71% in the high-vigour areas, 64% in the medium-low vigour areas (“Gran Selezione” selected bunches) and 56% in the medium-low vigour zone. The difference between the latter two is mainly linked to the health status of the grapes, which, in the selection of the best bunches, showed no signs of shrivelling or scorching. As for acidity, an opposite trend to vigour is observed: the lowest total acidity is recorded in the wine obtained from grapes harvested in the high-vigour areas. The higher acidity in low-vigour areas is due to the concentration effect caused by water loss due to high summer temperatures. This affected both the increase in alcohol content and the acid component. pH, on the other hand, remains substantially stable. Volatile acidity is lower in the Gran Selezione thanks to the better health status of the grapes, while malic acid is higher, confirming what was observed during sampling. As for sugars, these are lower in high-vigour zones and higher in low-vigour zones (less grapes but higher sugar concentration).

Table 1: Results of analyses on wines obtained from selective harvesting.

The wines obtained from the remaining bunches in the low-medium vigour areas have significant structure and softness, well balanced with good acidity, but with a very high alcohol percentage, decidedly above the current wine market standard. The wines obtained from grapes in the high-vigour areas, on the other hand, have a more vertical profile, with a less prominent structure that leaves room for the acid component, fruity aromas and a lower alcohol content. Finally, the “Gran Selezione” sits between the two previous wines, demonstrating that healthy grapes harvested in low-medium vigour zones give a balanced sensory profile, at the expense of quantity.

Conclusions

This case study shows how drone-based vigour maps can be used in a concrete way to guide selective harvesting, making it more objective and easily applicable in the field. The results confirm that vineyard variability is directly reflected in the quality of the grapes and wines, making it possible to obtain sensorially different batches from the same plot. In particular, healthy grapes from medium-low vigour areas give the wine a more balanced profile, while the other zones produced different, complementary profiles. Overall, integrating drone data with operational harvest management represents a concrete step towards a more efficient, data-driven viticulture. Field variability is no longer an obstacle, but a resource to be leveraged.

This case study was carried out by combining iDrone for aerial maps and iAgro for 3D plant scanning. If you manage a vineyard and want to apply the same approach to your business, contact us for a free consultation!