ArticleFrontiers in plant science2022
Machine Learning Strategies for the Retrieval of Leaf-Chlorophyll Dynamics: Model Choice, Sequential Versus Retraining Learning, and Hyperspectral Predictors.
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. Cited by 11 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
11 citing papers in PubMed, 41 citations in OpenAlex.
- Machine learning techniques for accessing the chlorophyll content and fluorescence in maize based on UAV multispectral sensor.BMC plant biology · 2026Article
- Phenology-aware collaborative retrieval of structural and physiological cotton traits from Sentinel-2 imagery using multi-task learning.Frontiers in plant science · 2026Article
- Large-scale non-destructive crown-level assessment of Ginkgo pigments via hyperspectral and machine learning techniques.Plant methods · 2025Article
- In-silico prediction of dislodgeable foliar residues and regulatory implications for plant protection products.Journal of exposure science & environmental epidemiology · 2025Article
- Mind the leaf anatomy while taking ground truth with portable chlorophyll meters.Scientific reports · 2025Article
- Combining the fractional order derivative and machine learning for leaf water content estimation of spring wheat using hyper-spectral indices.Plant methods · 2024Article
- Optimal-Band Analysis for Chlorophyll Quantification in Rice Leaves Using a Custom Hyperspectral Imaging System.Plants (Basel, Switzerland) · 2024Article
- Hyperspectral indices data fusion-based machine learning enhanced by MRMR algorithm for estimating maize chlorophyll content.Frontiers in plant science · 2024Article
- Machine Learning Methods for Automatic Segmentation of Images of Field- and Glasshouse-Based Plants for High-Throughput Phenotyping.Plants (Basel, Switzerland) · 2023Article
- Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methods.Frontiers in plant science · 2023Review
- Detecting Plant Stress Using Thermal and Optical Imagery From an Unoccupied Aerial Vehicle.Frontiers in plant science · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Monitoring leaf Chlorophyll (Chl)
Indexed as
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.