Innovation, algorithms, and satellites are allies for food security in the Mediterranean.

The combined use of artificial intelligence and remote sensing can support the transformation of the Mediterranean region's agroecosystem. This is revealed in a study by Wondimagegn doctoral student Abebe Demissie , along with professors Luca Sebastiani and Rudy Rossetto of the Institute of Plant Studies at the Scuola Superiore Sant'Anna in Pisa . Published in the European Journal of Agronomy , the research, explains a press release from the Pisan academic institution, "systematically analyzed 106 scientific papers to identify how models based on artificial intelligence and satellite data, particularly from the Sentinel, Modis, and Landsat missions , can predict crop productivity and assess plant growth dynamics with unprecedented robustness." Machine learning algorithms such as Random Forest, Support Vector Machines, and Neural Networks have proven particularly effective in analyzing satellite imagery, while the most recent deep learning models show great potential in integrating spatiotemporal data relating to crop stress and phenology. "The study," Sant'Anna continues, "emphasizes that hybrid approaches, combining data from satellites, drones, and field observations, offer the most promising prospects for precise and timely monitoring." However, the authors also highlight the need to promote greater data sharing, standardized validation methods, and closer collaboration among Mediterranean countries, particularly in the less represented regions of North Africa and the Middle East.