ArticleFrontiers in plant science2022
A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Bridging scales: integrated multi-omics and deep phenotyping for climate resilience in crop plants.Frontiers in plant science · 2026Review
- Intraspecific and intraindividual trait variability decrease with tree richness in a subtropical tree biodiversity experiment.Nature communications · 2025Article
- Divergent and stabilizing selection shape the phenotypic space of Arabidopsis thaliana.PLoS biology · 2025Article
- Hydro-Functional Traits and Their Dissimilarity to the Neighbourhood Buffer Tree Growth Against the 2018-2020 Central European Drought.Global change biology · 2025Article
- Maternal environmental effects and climate-smart seeds: unlocking epigenetic inheritance for crop innovation in the seed industry.The Plant journal : for cell and molecular biology · 2025Article
- Enhancing buckwheat maturity classification with generative adversarial networks for spectroscopy data augmentation.Frontiers in plant science · 2025Article
- Article
- NIRSpredict: a platform for predicting plant traits from near infra-red spectroscopy.BMC plant biology · 2024Article
- AraDiv: a dataset of functional traits and leaf hyperspectral reflectance of Arabidopsis thaliana.Scientific data · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
23 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
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