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README.md

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 ## Introduction
 
 <img align="left" width="200" height="200" src="https://github.com/tejakattenborn/AngleCAM/blob/main/illustrations_small.png">
-Vertical leaf surface angles are relevant for various applications (e.g., modelling radiative transfer in plants, producitvity, or Earth systems). Measuring or tracking leaf angles through time is, hence, of vital importance for various disciplines. AngleCam is a deep learning-based method to predict leaf angle distributions from horizontal photographs. AngleCam can be applied on single photographs or time series. AngleCam was evaluated over various species, growth forms and for predicting time series over several months at 3-min intervals under field conditions (see the the publication in Methdos in Ecology and Evolution for details: [![DOI](https://img.shields.io/badge/DOI-10.1111%252F2041----210X.13968-red)](https://doi.org/10.1111/2041-210X.13968)).
+Vertical leaf surface angles are relevant for various applications (e.g., modelling radiative transfer in plants, producitvity, or Earth systems). Measuring or tracking leaf angles through time is, hence, of vital importance for various disciplines. AngleCam is a deep learning-based method to predict leaf angle distributions from horizontal photographs. AngleCam can be applied on single photographs or time series. AngleCam was evaluated over various species, growth forms and for predicting time series over several months at 3-min intervals under field conditions.
 The underlying CNN models of AngleCam were trained with the Brinno TLC-200 Pro camera. The latter is low-cost and outdoor ready and enables to acquire time series up to several month. AngleCam may also be applicable to other cameras as long as their properties are comparable (Field of view, etc.).<br/>
 
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