From field variability to an operational map
On 28 September 2026, CREA presented the results of Hazelnut Orchard 5.0, a study carried out in a high-density organic hazelnut orchard in the Viterbo area at Azienda Agricola Gisella ed Elena Ascenzi. The work was part of the Agridigit and Agrofiliere projects and pursued a practical goal: identifying sections of the field with similar characteristics so they could be managed differently.
The research did not rely on a single image. The team combined apparent soil electrical conductivity measured by a tractor-mounted sensor with drone imagery, proximal measurements of vegetation vigour and direct checks on the plants. Combining these sources is what made the results useful from an agronomic perspective.
Two zones with different needs
The analysis distinguished two main zones. The first had sandier soil, less organic matter and a lower capacity to retain water. The second was more clay-rich and contained more organic matter, with better water and nutrient availability. The plants also responded differently: those in the second zone were more vigorous and had larger crowns on average.
This distinction can provide a basis for variable-rate or otherwise more localised interventions. Irrigation, fertilisation and pruning can be planned according to the actual conditions in each area instead of treating the whole field as uniform. The study points to this opportunity, but it does not promise an automatic or predetermined saving: rates and actions still require agronomic assessment.
What a drone survey can really do
A drone can rapidly reveal differences in crop vigour, cover and development across a large area. A colour variation on a map, however, does not explain its cause by itself. It may reflect soil, water, nutrition, disease or other factors. Ground measurements and a clearly defined technical question are therefore needed before the flight.
A useful workflow should:
- define the decision that the survey is intended to support;
- plan the flight under consistent and repeatable conditions;
- georeference images and outputs correctly;
- check anomalies observed from above directly in the field;
- compare data over time when monitoring is repeated.
Why this matters to farms
Hazelnut Orchard 5.0 demonstrates a mature use of drones: not merely as cameras, but as part of a measurement system. This direction is consistent with the European AgRI 2040 strategy, which aims to move digitalisation, artificial intelligence and robotics from research into solutions tested under real farming conditions.
In this type of project, Leoni Drone starts with the problem the farm needs to solve. The sensor, acquisition period, resolution and required field checks are selected only afterwards. This approach turns the survey into a comparable technical record that can support the agronomic professional, rather than simply an attractive image.
A drone creates value when an image becomes a comparable measurement and that measurement informs an agronomic decision.