Articles | Volume 23, issue 15
https://doi.org/10.5194/bg-23-5607-2026
https://doi.org/10.5194/bg-23-5607-2026
Research article
 | Highlight paper
 | 
14 Aug 2026
Research article | Highlight paper |  | 14 Aug 2026

AngleCam V2: Predicting leaf inclination angles across taxa from daytime and nighttime photos

Luis Kremer, Jan Pisek, Ronny Richter, Julian Frey, Daniel Lusk, Christiane Werner, Christian Wirth, and Teja Kattenborn

Data sets

AngleCam V2: Predicting leaf inclination angles across taxa from daytime and nighttime photos Teja Kattenborn et al. https://doi.org/10.5281/zenodo.17086253

Model code and software

AngleCam V2 pretrained model Luis Kremer and Teja Kattenborn https://doi.org/10.5281/zenodo.17101166

AngleCam V2 source code L. Kremer and T. Kattenborn https://doi.org/10.5281/zenodo.21700815

Download
Editorial statement
Leaf inclination angle distribution is a key parameter for radiative transfer modeling and for proximal and remote sensing applications, and is an often-overlooked response of plants to stress in the short term (diurnal dynamics). Traditionally, measuring leaf inclination angles is labor-intensive. AngleCamV2 provides a continuous, low-cost system to assess leaf angle distributions that can be used to better constrain the retrieval of plant traits from radiative transfer model inversions, interpret signals such as sun-induced fluorescence that is highly dependent on the canopy architecture, and understand leaf inclination dynamics under water stress.
Short summary
To adapt to changing environmental conditions, plants can adjust their leaf angles. We developed AngleCam V2, an AI method that estimates leaf inclination angles from photos taken during day and night. Trained on thousands of images from about 200 species, it monitors daily changes in leaf angle, aligns with laser-scanning data, and detects systematic shifts under water limitation. AngleCam V2 provides an open-source tool for monitoring leaf angle dynamics over time, taxa, and environments.
Share
Altmetrics
Final-revised paper
Preprint