AI-Driven Responsive Observation and Optimization of Turf Systems (AID-ROOTS)
Short description
AID-ROOTS is a collaborative research project between the University of Gothenburg, Capillary AB, and Simplif-AI Consulting AB. The project investigates how artificial intelligence, environmental sensing, data fusion, outdoor hydroponics and predictive modeling can improve the management and performance of sports fields and turf systems.
By combining environmental, biological, operational, and sensor data, the project aims to develop intelligent models capable of monitoring field conditions, predicting future performance, and supporting data-driven decision making. Hydroponics has revolutionized greenhouse growing during the past decades. Now, large-scale outdoor hydroponics systems also have the potential to radically change the way we grow any plants, but the control must be diligent and smart. The research contributes to more sustainable use of water, energy, and resources while advancing scientific knowledge in environmental intelligence and AI-enabled green infrastructure.
Background
Sports fields and turf systems are complex living environments influenced by weather, soil conditions, water availability, biological activity, maintenance practices, and operational use. Increasing demands on field quality, sustainability, and resource efficiency create a need for new methods such as hydroponics. The downside and reason why hydroponics has not been possible for outdoor use so far is the need for constant control as growing conditions can change rapidly in these systems. Now, with new technologies available, we are capable of understanding and managing these systems using advanced digital technologies.
Recent advances in artificial intelligence, environmental sensing, and data analytics provide opportunities to transform large volumes of environmental and operational data into actionable insights. However, significant research challenges remain regarding how heterogeneous data sources can be integrated, interpreted, and utilized to support decision making in biological systems.
Purpose
The purpose of AID-ROOTS is to develop and evaluate AI-based methods for environmental monitoring, data fusion, predictive modeling, and optimization of sports field and turf systems.
Research questions
- How can environmental, biological, and operational data be combined to improve understanding of turf system performance?
- How can AI-based models predict future field conditions and emerging risks?
- How can data-driven decision support improve sustainability, resource efficiency, and field quality?
- How can synthetic environmental indicators and predictive analytics support intelligent turf management?
Methods
- Environmental sensing and monitoring (including use of drones to capture top-down images of turf quality)
- Data fusion and machine learning (multi-modal modelling of data)
- Predictive modeling
- Synthetic data generation
- Validation through real-world sports field environments (living laboratories)
- AI-supported decision support and optimization
Participants
Internal: Arash Darakhsh
External: Ehsan Zaiemzadeh, https://www.capillaryflow.com/
