Introduction SierTech develops advanced crop monitoring solutions based on AI-driven analysis of images from drones, fixed cameras, and mobile devices for ornamental crop growers. The use cases focus on transforming multi-source imaging data into actionable insights, enabling early intervention and more efficient crop management. The detection of biotic and abiotic plant stress and of growth anomalies in large production fields are the main topics investigated. This will enable productivity improvements, reduce input use, and enhance sustainability. By validating the developed models and methods across different crops and in different production systems we will deploy solutions with a broad application scope. Methodologies • Images and UAV-based images (RGB, MS, …) collected in real-world production fields are gathered. • Generic AI Deep Learning models are developed for the automatic biotic and abiotic plant stress and of growth anomalies – if any other issues raise during the production cycle, extra traits might be considered in agreement with the grower. • Derived information is used for decision support during the crop production phase (e.g. place-specific fertilization or disease management), quality assessment (e.g. phenology, flowering intensity, plant shape) and stock management. Output The project contributes to scalable digital agriculture solutions by linking sensing technologies with intelligent data analytics. Impact Develop a toolbox that enables ornamental crop growers to exploit innovative technologies for improving the cultivation process and stock management. What we offer • Protocols for UAV flights, that are flexible enough to accommodate the analysis and interpretation of images acquired in non-controlled environments but enabling the extraction of detailed information tailored to the needs of the sector. • Translate scientifically-sound insights and procedures to easy and cheap to implement protocols based on the use of cheap sensors for use in a grower’s environment. What we need • Similar datasets of ornamental crops to develop more generic AI models. • Access to drones for use in greenhouse, including MS and thermal sensors for indoor use. • Cheap drone in a box systems with associated data management that does not require data sharing with external systems. • Tools to implement developed models into smartphone apps.

Source: Annual AgData Use Case Summit 2026 booklet.