Evidence map›Paper›PMID 37268945›Full record

ArticlePlant methods2023

Non-destructive pre-symptomatic detection of gray mold infection in kiwifruit using hyperspectral data and chemometrics.

Najmeh Haghbin, Adel Bakhshipour, Hemad Zareiforoush, Sedigheh Mousanejad

Abstract read
In one paragraph

Article in Plant methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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

6 citing papers in PubMed.

  1. Detection of Hidden Pest Rice Weevil (Foods (Basel, Switzerland) · 2026
    Article
  2. Physiological and image-based characterization ofFrontiers in plant science · 2026
    Article
  3. Article
  4. Review
  5. Article
  6. Article
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.

Najmeh HaghbinDepartment of Biosystems Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran.
Adel BakhshipourDepartment of Biosystems Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran. abakhshipour@guilan.ac.ir.
Hemad ZareiforoushDepartment of Biosystems Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran.
Sedigheh MousanejadDepartment of Plant Protection, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Application of hyperspectral imaging (HSI) and data analysis algorithms was investigated for early and non-destructive detection of Botrytis cinerea infection. Hyperspectral images were collected from laboratory-based contaminated and non-contaminated fruits at different day intervals. The spectral wavelengths of 450 nm to 900 nm were pretreated by applying moving window smoothing (MWS), standard normal variates (SNV), multiplicative scatter correction (MSC), Savitzky-Golay 1

Indexed as

Early detectionFungal infectionHyperspectral imagingKiwifruitWavelength selection

Identifiers

PMID37268945
PMCPMC10236597

What OpenQuestion holds

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LicenceCC BY
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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.