ArticleFrontiers in plant science2025
Integrated diagnostics and time series sensitivity assessment for growth monitoring of a medicinal plant (
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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Remote Sensing Applications in Medicinal Plant Monitoring and Quality Assessment: A Review.Sensors (Basel, Switzerland) · 2026Review
- Prediction of growth parameters in the medicinal plantFrontiers in plant science · 2026Article
- Unmanned aerial vehicle payload technology applications in agriculture and other low-altitude scenarios: a review.Frontiers in plant science · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Water and nitrogen are essential elements prone to deficiency during plant growth. Current water-fertilizer monitoring technologies are unable to meet the demands of large-scale Methods: This study collected UAV multispectral images of Results: The results demonstrated that both the RF algorithm and excess green index (EXG) exhibited versatility in growth monitoring and yield prediction. PIs collectively achieved high-precision predictions (mean 0.42 ≤ Conclusions: This research established a high-precision, time-efficient, and practical UAV remote sensing-based method for growth monitoring and yield prediction in
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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 OpenQuestion graph.