ArticleFrontiers in plant science2025
Prediction of harvest-related traits in barley using high-throughput phenotyping data and machine learning.
Article in Frontiers in plant science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Wild barley cytoplasms reduce grain weight plasticity, with environment-dependent cytonuclear epistasis at theMolecular breeding : new strategies in plant improvement · 2026Article
- Mulberry Drought Diagnosis: Integrating Proximal Sensing and Metabolomics for Remote Monitoring.Plants (Basel, Switzerland) · 2026Article
- DCSFormer: a high-precision method for cotton seedling point cloud organ segmentation.Frontiers in plant science · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Developing crop varieties that maintain productivity under drought is essential for future food security. Here, we investigated the potential of time-resolved high-throughput phenotyping to predict harvest-related traits and identify drought-stressed plants. Six barley lines (
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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.