A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy

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...

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Published in:Frontiers in plant science 2022-05, Vol.13, p.836488-836488
Main Authors: 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
Format: Article
Language:English
Subjects:
Arabidopsis thaliana
functional traits
Life Sciences
machine learning
metabolomics
multivariate analysis
near-infrared spectroscopy (NIRS)
Plant Science
Vegetal Biology
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Summary: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.
ISSN:1664-462X
1664-462X
DOI:10.3389/fpls.2022.836488