Online/In-Person Course
Date and times
From February 23 to 26, 2026
Schedule:
- Mondays from 4:00 PM to 9:00 PM (Online)
- Tuesday 4:00 PM to 9:00 PM (Online)
- Wednesdays from 9:00 a.m. to 1:00 p.m. and from 3:00 p.m. to 6:00 p.m. (In person)
- Thursdays from 9:00 to 12:00 (In person)
Registration will be open until February 18, 2026.
Free training.
This course addresses the application of digital twins in agriculture, focusing on water and carbon as primary areas of application, in line with the metrics used in agricultural and climate policies. Using structural variables derived from LiDAR, it introduces fundamental concepts related to efficient water management, biomass estimation, carbon sequestration, and carbon footprinting, connecting them to simplified growth models and approaches used in environmental inventories and reporting systems. The course has a highly practical and applied approach, geared towards understanding the architecture of digital twins and the operational use of structural information, without requiring prior knowledge of programming or advanced modeling.
More information on our website.
Who is it aimed at?
- Technicians and advisors in the agricultural sector who wish to incorporate advanced digital tools for the structural characterization of crops and the improvement of decision-making in agricultural systems.
- Professionals from technology and agricultural services companies interested in the development and implementation of solutions based on LiDAR, spatial analysis and digital twins applied to agriculture.
- University and academic field: undergraduate and postgraduate students, research staff and teaching staff who want to acquire a conceptual and practical foundation on agricultural digital twins and the use of LiDAR technologies.
- Public officials and those responsible for agricultural planning and water management who work in agricultural policies, irrigation modernization and sustainability, and who need to understand the potential of digital twins and 3D remote sensing in the efficient management of agricultural systems.