Data_Sheet_1_A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy.pdf (300.66 kB)
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Data_Sheet_1_A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy.pdf

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posted on 20.05.2022, 05:15 authored by François Vasseur, Denis Cornet, Grégory Beurier, Julie Messier, Lauriane Rouan, Justine Bresson, Martin Ecarnot, Mark Stahl, Simon Heumos, Marianne Gérard, Hans Reijnen, Pascal Tillard, Benoît Lacombe, Amélie Emanuel, Justine Floret, Aurélien Estarague, Stefania Przybylska, Kevin Sartori, Lauren M. Gillespie, Etienne Baron, Elena Kazakou, Denis Vile, Cyrille Violle

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.

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