ArticleScientific reports2021
Deep learning and citizen science enable automated plant trait predictions from photographs.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Leveraging remote sensing and crowd-sourced biodiversity data for enhanced plant functional trait mapping.Nature communications · 2026Article
- Integrating Genomics, Collections, and Community Science to Delimit Species Clarifies the Taxonomy of a Variable Monitor Lizard (Varanus tristis).Systematic biology · 2026Article
- Crowdsourced biodiversity monitoring fills gaps in global plant trait mapping.Nature communications · 2026Article
- A vision language model for generating XML-based organ-level plant architecture representations of cowpea from simulated images.Frontiers in artificial intelligence · 2026Article
- Impacts of leaf traits on vegetation optical properties in Earth system modeling.Nature communications · 2025Article
- Article
- Evaluating the method reproducibility of deep learning models in biodiversity research.PeerJ. Computer science · 2025Article
- Global patterns of plant functional traits and their relationships to climate.Communications biology · 2024Article
- Artificial intelligence for geoscience: Progress, challenges, and perspectives.Innovation (Cambridge (Mass.)) · 2024Review
- Crop Identification Using Deep Learning on LUCAS Crop Cover Photos.Sensors (Basel, Switzerland) · 2023Article
- Temporal stability of the hybrid zone betweenEcology and evolution · 2023Article
- Applicability of Point- and Polygon-Based Vegetation Monitoring Data to Identify Soil, Hydrological and Climatic Driving Forces of Biological Invasions-A Case Study ofPlants (Basel, Switzerland) · 2023Article
- Using large language models to extract plant functional traits from unstructured text.Applications in plant sciencesArticle
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plant functional traits ('traits') are essential for assessing biodiversity and ecosystem processes, but cumbersome to measure. To facilitate trait measurements, we test if traits can be predicted through visible morphological features by coupling heterogeneous photographs from citizen science (iNaturalist) with trait observations (TRY database) through Convolutional Neural Networks (CNN). Our results show that image features suffice to predict several traits representing the main axes of plant functioning. The accuracy is enhanced when using CNN ensembles and incorporating prior knowledge on trait plasticity and climate. Our results suggest that these models generalise across growth forms, taxa and biomes around the globe. We highlight the applicability of this approach by producing global trait maps that reflect known macroecological patterns. These findings demonstrate the potential of Big Data derived from professional and citizen science in concert with CNN as powerful tools for an efficient and automated assessment of Earth's plant functional diversity.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.