AngleCam - Tracking leaf angle distributions through time with image series and deep learning

Vertical leaf angles and their temporal variation are directly related to multiple ecophysiological and environmental processes and properties. However, there is no efficient method for tracking leaf angles of plant canopies under field conditions.

Here, we present AngleCam, a method to estimate leaf angle distributions from horizontal photographs acquired with timelapse cameras and deep learning. The AngleCam is a pattern recognition model based on convolutional neural networks and was trained with leaf angle distributions obtained from visual interpretation of more than 2500 plant photographs across different species and scene conditions.

Leaf angle predictions were evaluated over a wide range of species, plant functional types and scene conditions using independent samples from visual interpretation (R2 = 0.84). Moreover, the method was evaluated using leaf angle estimates obtained from terrestrial laser scanning (R2 = 0.75). AngleCam was successfully tested under field-conditions for the long-term monitoring of leaf angles for two broadleaf tree species in a temperate forest. The plausibility of the predicted leaf angle time series was underlined by its close relationship with environmental variables related to transpiration. Moreover, showed that the variation in leaf angles resembles changes in several leaf-water related traits.

The evaluations showed that AngleCam is a robust and efficient method to track leaf angles under field conditions. The output of AngleCam is compatible and relevant for with a range of applications, including functional-structural plant modelling, Earth system modelling or radiative transfer modelling of plant canopies. AngleCam may also be used to predict leaf angle distributions from existing data, such as curated in PhenoCam networks or citizen science projects.