AUTHOR=Vasseur François , Cornet Denis , Beurier Grégory , Messier Julie , Rouan Lauriane , Bresson Justine , Ecarnot Martin , Stahl Mark , Heumos Simon , Gérard Marianne , Reijnen Hans , Tillard Pascal , Lacombe Benoît , Emanuel Amélie , Floret Justine , Estarague Aurélien , Przybylska Stefania , Sartori Kevin , Gillespie Lauren M. , Baron Etienne , Kazakou Elena , Vile Denis , Violle Cyrille TITLE=A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy JOURNAL=Frontiers in Plant Science VOLUME=13 YEAR=2022 URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2022.836488 DOI=10.3389/fpls.2022.836488 ISSN=1664-462X ABSTRACT=

The trait-based approach in plant ecology aims at understanding and classifying the diversity of ecological strategies by comparing plant morphology and physiology across organisms. The major drawback of the approach is that the time and financial cost of measuring the traits on many individuals and environments can be prohibitive. We show that combining near-infrared spectroscopy (NIRS) with deep learning resolves this limitation by quickly, non-destructively, and accurately measuring a suite of traits, including plant morphology, chemistry, and metabolism. Such an approach also allows to position plants within the well-known CSR triangle that depicts the diversity of plant ecological strategies. The processing of NIRS through deep learning identifies the effect of growth conditions on trait values, an issue that plagues traditional statistical approaches. Together, the coupling of NIRS and deep learning is a promising high-throughput approach to capture a range of ecological information on plant diversity and functioning and can accelerate the creation of extensive trait databases.