Olive Decision Tool

The overall objective of the project is to develop a methodology that allows new data to be generated for the olive industry and oil mills from early stages, using predictive models supported by trained artificial intelligence in spectral databases.

Farm challenges

Anticipate olive harvest and quality at early stages, integrate heterogeneous drone and satellite data in a scalable manner, and manage high agronomic and climatic variability to optimize the optimal harvest date.

Assistance / Boost program

ADER IDI.

Innovative features of the initiative / solution

A pioneering combination of artificial intelligence with spectral data from drones and satellites, including correction and early prediction models that enable harvest estimates, quality assessments, and optimal harvesting times to be calculated in a scalable and replicable manner in olive groves.

Results obtanied

Develodevelopment and validation of AI-based predictive models that accurately estimate olive yield, quality parameters, and optimal harvest timing in advance, enabling improved decision-making and scalable satellite-based monitoring for olive growers and mills.pment and validation of AI-based predictive models that accurately estimate olive yield, quality parameters, and optimal harvest timing in advance, enabling improved decision-making and scalable satellite-based monitoring for olive growers and mills.

Lessons learned

Combining agronomic expertise with AI and multi-source spectral data significantly improves early decision-making, while robust models require adaptive strategies to manage climatic variability and data availability in real farming conditions.

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