From leaf to label: a robust automated workflow for stomata detection.
Abstract
1. Plant leaf stomata are the gatekeepers of the atmosphere-plant
interface and are essential building blocks of land surface models as
they control transpiration and photosynthesis. Although more stomatal
trait data is needed to significantly reduce the error in these model
predictions, recording these traits is time-consuming and no
standardized protocol is currently available. Some attempts were made to
automate stomatal detection from photomicrographs, however, these
approaches have the disadvantage of using classic image processing or
targeting a narrow taxonomic entity which makes these technologies less
robust and generalizable to other plant species. We propose an
easy-to-use and adaptable workflow from leaf to label. A methodology for
automatic stomata detection was developed using deep neural networks
according to the state-of-the-art and its applicability demonstrated
across the phylogeny of the angiosperms. 2. We used a patch-based
approach for training/tuning three different deep learning
architectures. For training, we used 431 micrographs taken from leaf
prints made according the nail polish method from herbarium specimens of
19 species. The best performing architecture was tested on 595 images of
16 additional species spread across the angiosperm phylogeny. 3. The
nail polish method was successfully applied in 78% of the species
sampled here. The VGG19 architecture slightly outperformed the basic
shallow and deep architectures, with a confidence threshold equal to 0.7
resulting in an optimal trade-off between precision and recall. Applying
this threshold the VGG19 architecture obtained an average F-score of
0.87, 0.89 and 0.67 on the training, validation and unseen test set,
respectively. The average accuracy was very high (94%) for computed
stomatal counts on unseen images of species used for training. 4. The
leaf-to-label pipeline is an easy-to-use workflow for researchers of
different areas of expertise interested in detecting stomata more
efficiently. The described methodology was based on multiple species and
well-established methods so that it can serve as a reference for future
work.