Since its founding in 2020, the TUM Precision Agriculture Lab has been committed to empower smart farming research, education, and practice.
We are delighted to share that Dr. Anirudh Belwalkar has been awarded a Young Researcher and Innovator Conference Grant under the EU COST Action to attend the IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2025) in Australia. The grant, amounting to 1,500 EUR, supports his participation in IGARSS2025. Congratulations to Anirudh for this well-deserved recognition and opportunity to share his research on drone-based hyperspectral remote sensing! ππ
Snapshots from our UAV field campaign, combining drone-based hyperspectral imaging with in-situ measurements.
π°οΈπ± Boosting Nitrogen Use Efficiency from Space π±π°
From ground to sky β our satellite remote sensing projects (GreenWindows, LiveSen-MAP, PHENET) are paving the way for precision nitrogen fertilization!ππ‘
Our recent publication Gackstetter, Yu, KΓΆrner 2026 ISPRS addresses the critical challenge of improving satellite image classification when target labels are scarce, a common issue in remote sensing. We focus on developing effective methods for unsupervised domain adaptation (UDA), specifically through our proposed model, RAINCOAT-SRS, which builds upon existing UDA techniques tailored for satellite image time series (SITS). This work connects with our previous work while enhancing classification accuracy by employing self-attention mechanisms and frequency-augmented features. By comparing RAINCOAT-SRS with other leading algorithms like TimeMatch, we discovered that our model outperforms standard methods. This work emphasizes the importance of domain stability and structural patterns rather than merely linear shifts between domains, contributing significant insights into UDA effectiveness. Our findings provide a novel framework for tackling the complexities of SITS classification, promoting more effective cross-regional and multi-temporal analyses.
Our recently accepted two papers by Wang J et al. (2025) and Wang F et al. (2025) investigated drone remote sensing data-driven models for crop nitrogen estimation.
Both works utilize drone remote sensing technology to explore nitrogen dynamics in wheat, aiming to enhance nitrogen use efficiency (NUE) in agriculture.