Building on its digital infrastructure, the second phase of the SOLEATECH project—developed in collaboration with Bahçeşehir University (Türkiye) and funded under the bilateral call PRIMA Xjenza Malta–TÜBİTAK 2023—focuses on transforming raw data into actionable insights through Artificial Intelligence. By integrating advanced analytics into a Decision Support System, the project empowers farmers to make precise, timely decisions.

Two core AI models drive this innovation. A Long Short-Term Memory (LSTM) model forecasts soil water potential, enabling proactive irrigation planning. In parallel, an Artificial Neural Network (ANN) evaluates environmental variables to predict olive quality, categorizing outputs into four performance levels. To enhance accuracy and scalability, techniques such as Principal Component Analysis reduce data complexity while maintaining predictive power.

To bridge the gap between technology and practice, the system incorporates a Multi-Criteria Decision-Making framework. This approach combines AI output with agronomic expertise, identifying irrigation strategy as the most critical factor for productivity.

The expected impact is significant: a 20% increase in productivity alongside a 20% reduction in water use and operational costs. Beyond efficiency, SOLEATECH supports knowledge transfer and policy development, contributing to the long-term sustainability of Mediterranean olive farming.

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