Modern agriculture is under increasing pressure to meet rising global food demand while minimizing environmental impacts. Ensuring the sustainability and resilience of agricultural systems requires access to accurate, timely, and spatially detailed information. However, such data remain limited. This session highlights the growing role of remote sensing, artificial intelligence (AI), and machine learning (ML) in transforming agricultural monitoring and decision-making.

We invite abstract submissions focused on, but not limited to, the following areas:

• Applications of remote sensing for crop monitoring, yield estimation, and land-use mapping
• Development and integration of AI/ML and deep learning models in agriculture
• Use of open-source satellite data (e.g., Sentinel, Landsat, MODIS) and agricultural datasets
• Data fusion techniques combining remote sensing, weather, soil, and socio-economic datasets

This session aims to bring together researchers, practitioners, and policymakers to foster interdisciplinary dialogue on building sustainable, climate-resilient agri-food systems.