  Plant phenotyping is the assessment of a plant's traits and plant
identification is the process of determining the category such as genus and
species. In this paper we present an interpretable neural network trained on
the UPWINS spectral library which contains spectra with rich metadata across
variation in species, health, growth stage, annual variation, and environmental
conditions for 13 selected indicator species and natural common background
species. We show that the neurons in the network learn spectral indicators for
chemical and physiological traits through visualization of the network weights,
and we show how these traits are combined by the network for species
identification with an accuracy around 90% on a test set. While neural networks
are often perceived as `black box' classifiers, our work shows that they can be
in fact more explainable and informative than other machine learning methods.
We show that the neurons learn fundamental traits about the vegetation, for
example the composition of different types of chlorophyll present which
indicates species as well as response to illumination conditions. There is
clear excess training capacity in our network, and we expect that as the UPWINS
spectral library continues to grow the approach in this paper will provide
further foundational insights in understanding plant traits. This provides a
methodology for designing and interpreting neural networks on spectral data in
general, and provides a framework for using neural networks with hyperspectral
imagery for understanding vegetation that is extendable to other domains.
