ArticlePlant phenomics (Washington, D.C.)2023
Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits in
Article in Plant phenomics (Washington, D.C.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
13 citing papers in PubMed, 13 citations in OpenAlex.
- A prototype-augmented few-shot segment anything model for field phenotyping of lettuce germplasm from UAV imagery.Plant phenomics (Washington, D.C.) · 2026Article
- Genome shuffling enables quantitative trait locus mapping in Bacillus subtilis.Nature communications · 2026Article
- Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026Review
- Artificial Intelligence Methods in Forest Biotechnology: Current Status and Future Prospects.International journal of molecular sciences · 2026Review
- Higher Wood Density Lowers Feedstock Cost and Has Minimal Impact on Biomass Conversion to Biofuels.ACS sustainable chemistry & engineering · 2026Article
- Revealing hierarchical structure of leaf venations via diffusion-refined label-efficient segmentation: Dataset and method.Plant phenomics (Washington, D.C.) · 2026Article
- Identification of intraspecific cultivarFrontiers in plant science · 2026Article
- Climate adaptation in Populus trichocarpa: key adaptive loci identified for stomata and leaf traits.The New phytologist · 2025Article
- Improving plant breeding through AI-supported data integration.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- A spontaneous keypoints connection algorithm for leafy plants skeletonization and phenotypes extraction.Frontiers in plant science · 2025Article
- Phenotyping of Drought-Stressed Poplar Saplings Using Exemplar-Based Data Generation and Leaf-Level Structural Analysis.Plant phenomics (Washington, D.C.) · 2024Article
- Auto-LIA: The Automated Vision-Based Leaf Inclination Angle Measurement System Improves Monitoring of Plant Physiology.Plant phenomics (Washington, D.C.) · 2024Article
- Few-Shot Learning Enables Population-Scale Analysis of Leaf Traits inPlant phenomics (Washington, D.C.) · 2023Article
Corrections and comments
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Authors and funding
13 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plant phenotyping is typically a time-consuming and expensive endeavor, requiring large groups of researchers to meticulously measure biologically relevant plant traits, and is the main bottleneck in understanding plant adaptation and the genetic architecture underlying complex traits at population scale. In this work, we address these challenges by leveraging few-shot learning with convolutional neural networks to segment the leaf body and visible venation of 2,906
Identifiers
What OpenQuestion 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 OpenQuestion graph.