Research / SYNTERA Agri
AI for Agriculture
Why it matters
Food systems face climate pressure and volatile markets. AI can help farmers and policy makers see problems sooner and plan with better forecasts.
Key challenges
- Limited labelled field data
- Robust vision models in changing outdoor conditions
- Reliable forecasting under market and weather shocks
Our approach
Computer vision, robotics and autonomous systems, together with machine learning and time-series forecasting for agricultural data.
Publications
Selected publications
Temporal feature engineering for agricultural commodity price forecasting in Nigeria: Evaluating machine learning, deep learning and time-series approaches
Ogunjobi, D.; Shibl, R.; Saremi, S.
Journal of Agribusiness in Developing and Emerging Economies, 1–28
Team
Who works on this
Dr. Shahrzad Saremi
Founder · Director
School of Science, Technology and Engineering, University of the Sunshine Coast
Professor Dr. Rania Shibl
Co-Director · Cofounder
Faculty of Science and Engineering, Southern Cross University, Lismore
Tobias Romano
Researcher
UNICEN
Damilare Ogunjobi
Researcher
Department of Data Science and Analytics, BI Norwegian Business School, Oslo