PrecisionAg Lab @ Uni.lu Workshop: Hyperspectral Remote Sensing & AI in Agriculture

The TUM Precision Agriculture Lab (PAG Lab) joined the scientific workshop “Hyperspectral Remote Sensing and AI in Agriculture” hosted by the University of Luxembourg (Campus Kirchberg, 4–5 February 2026). Our group delivered three talks connecting hyperspectral sensing, biophysical trait and SIF retrieval, and robust sensor calibration and AI pipelines for precision crop management.

Three Talks in the Precision Crop Management Session

Remote sensing of plant traits for nitrogen nutrition and yield estimation

Kang Yu gave a keynote talk and shared our lab’s work for trait-based crop monitoring that goes beyond generic vegetation indices—linking spectroscopy to interpretable variables relevant for nitrogen (N) nutrition, productivity, and management. We discussed how combining physics-informed approaches (e.g., radiative transfer concepts) with machine learning can improve generalization across environments and varieties, and help translate monitoring into practical recommendations and sensor development.

Assessing UAV-derived narrow-band SIF to characterize nitrogen use efficiency in wheat

Anirudh Belwalkar’s talk focused on narrow-band solar-induced fluorescence (SIF) from UAV platforms and its potential to characterize nitrogen use efficiency (NUE) signals in wheat. The talk connected physiological interpretation (photosynthetic functioning) with operational sensing constraints, discussing when UAV SIF can add value beyond reflectance-only products for N-related monitoring.

From reflectance to crop phenotype: quantifying uncertainty from UAV atmospheric correction methods

Wuhua Wang discussed the importance of hyperspectral data calibration and uncertainty estimates. This talk examined how different UAV atmospheric correction methods propagate uncertainty into downstream phenotyping and trait retrieval—a critical issue when hyperspectral workflows move from controlled experiments toward multi-site deployment.

Why This Workshop Mattered for Our Lab

The Uni.lu workshop brought together experts across hyperspectral sensing, UAV platforms, AI/ML, crop health, and nutrient strategies—creating a strong space for benchmarking methods, discussing data sharing and cross validation, and aligning on what “decision-ready” remote sensing should look like. For our PAG Lab, it was especially valuable to exchange views on Bridging physics + AI for stronger transferability (across years, sites, genotypes).

Thanks & Next Steps

We thank the University of Luxembourg, Prof. Teferle’s team and the workshop organisers for hosting a focused, high-quality event.

If you are interested in collaboration on hyperspectral trait retrieval, UAV SIF, or nitrogen decision support, feel free to reach out—we are keen to build shared datasets, benchmarks, and reproducible workflows. *— TUM Precision Agriculture Lab *