Evidence map›Paper›PMID 40510659›Full record

ArticleJournal of plant diseases and protection : scientific journal of the German Phytomedical Society (DPG)2025

Detection of aphid infestation on faba bean (

Ali Saeidan, John Caulfield, Jozsef Vuts, Ni Yang, Ian Fisk

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of plant diseases and protection : scientific journal of the German Phytomedical Society (DPG), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Ali SaeidanInternational Flavour Research Centre, Division of Food, Nutrition and Dietetics, University of Nottingham, Sutton Bonington Campus, Loughborough, LE12 5RD UK.
John CaulfieldBiological Chemistry Department, Rothamsted Research, Harpenden, Hertfordshire AL5 2JQ UK.
Jozsef VutsDepartment of Bio Interactions and Crop Protection, Rothamsted Research, Harpenden, AL52JQ UK.
Ni YangInternational Flavour Research Centre, Division of Food, Nutrition and Dietetics, University of Nottingham, Sutton Bonington Campus, Loughborough, LE12 5RD UK.
Ian FiskInternational Flavour Research Centre, Division of Food, Nutrition and Dietetics, University of Nottingham, Sutton Bonington Campus, Loughborough, LE12 5RD UK.ORCID 0000-0001-8448-3123

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aphids hide under leaves, reproduce rapidly, and require early detection to prevent crop damage, disease transmission, and ensure effective pest management. This study presents a novel approach for aphid detection by utilizing hyperspectral imaging, multivariate classification methods and spectral information divergence (SID) analyses. The hyperspectral images average spectrum (

Indexed as

Aphid detectionHyperspectral imagingMachine learningSpectral information divergence

Identifiers

PMID40510659
PMCPMC12152095

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.