Evidence map›Paper›PMID 27994607›Full record

ArticleFrontiers in plant science2016

Multicolor Fluorescence Imaging as a Candidate for Disease Detection in Plant Phenotyping.

María L Pérez-Bueno, Mónica Pineda, Francisco M Cabeza, Matilde Barón

Open access · goldAbstract read
In one paragraph

Article in Frontiers in plant science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
8.9field-weighted citation impact, top 3% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed, 62 citations in OpenAlex.

  1. Harnessing artificial intelligence in plant breeding: innovations in digital phenotyping and breeding methodologies.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

María L Pérez-BuenoDepartment of Biochemistry, Cellular and Molecular Biology of Plants, Estación Experimental del Zaidín - Spanish Council of Scientific Research Granada, Spain.
Mónica PinedaDepartment of Biochemistry, Cellular and Molecular Biology of Plants, Estación Experimental del Zaidín - Spanish Council of Scientific Research Granada, Spain.
Francisco M CabezaDepartment of Biochemistry, Cellular and Molecular Biology of Plants, Estación Experimental del Zaidín - Spanish Council of Scientific Research Granada, Spain.
Matilde BarónDepartment of Biochemistry, Cellular and Molecular Biology of Plants, Estación Experimental del Zaidín - Spanish Council of Scientific Research Granada, Spain.
Estación Experimental del Zaidín · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The negative impact of conventional farming on environment and human health make improvements on farming management mandatory. Imaging techniques are implemented in remote sensing for monitoring crop fields and plant phenotyping programs. The increasingly large size and complexity of the data obtained by these techniques, makes the implementation of powerful mathematical tools necessary in order to identify informative parameters and to apply them in precision agriculture. Multicolor fluorescence imaging is a useful approach for the study of plant defense responses to stress factors at bench scale. However, it has not been fully applied to plant phenotyping. This work evaluates the possible application of multicolor fluorescence imaging in combination with thermography for the particular case of zucchini plants affected by soft-rot, caused by

Indexed as

artificial neural networkCucumis meloCucurbita pepoDickeya dadantiilogistic regression analysismulticolor fluorescence imagingphenotypingthermal imaging

Identifiers

PMID27994607
PMCPMC5134354
OpenAlexW2558488867

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.