Understanding how plants respond to controlled environments is fundamental to CEA optimisation. DiAGRI researchers study genotype × environment (GxE) interactions, abiotic stress responses (temperature, light, drought), and yield optimisation across a range of crop species. The precision control of our growth chambers enables factorial experiments that isolate specific environmental variables.
Developing plant varieties specifically optimised for controlled environments — with traits such as compact architecture, efficient light use, rapid growth cycles, and enhanced nutritional profiles. DiAGRI leverages genome editing technologies (e.g., CRISPR) to accelerate the development of CEA-adapted cultivars, with strawberry as a primary model crop.
Integrating IoT sensor networks with intelligent automation to create responsive growing environments. Research focuses on developing closed-loop control systems where environmental parameters (temperature, humidity, light spectrum, CO₂, nutrients) are dynamically adjusted based on real-time plant status data from imaging and sensor feedback.
Developing AI methods that combine data from multiple sensing modalities — 3D geometry, hyperspectral reflectance, thermal signatures, fluorescence, and environmental sensors — into unified models for plant phenotyping, growth prediction, and stress detection. Research explores CLIP-based multi-modal alignment, edge AI deployment, and predictive analytics.
Building virtual replicas of physical growth environments that integrate real-time sensor data with AI-driven predictive models. Digital twins enable in-silico experimentation — testing hypothetical growing conditions, optimising resource allocation, and accelerating experimental design without occupying physical infrastructure.
Investigating the food science dimensions of CEA — including nutritional quality, sensory attributes, and post-harvest handling of controlled-environment crops. Also exploring scalable business models, decentralised food systems, and food design approaches for urban food production in collaboration with the Viikki Food Design Factory.
DiAGRI deploys artificial intelligence at the edge — processing imaging and sensor data directly within the growth chamber facility for real-time insights and automated control. Our AI research spans multi-modal data fusion, predictive analytics, and embedded model deployment.
Digital twins are virtual replicas of physical systems, continuously updated with real-time data. In DiAGRI, we develop digital twins of our growth chamber environments — enabling researchers and growers to simulate, predict, and optimise crop production without the constraints of physical experimentation.