Evidence map›Paper›PMID 41608050›Full record

ArticleData in brief2026

CocoaMoniliaDataSet: A cocoa pod dataset to detect and classify

Joan Alvarado, Juan Felipe Restrepo-Arias, David Velásquez, John W Branch-Bedoya, Mikel Maiza

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Data in brief, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Joan AlvaradoEscuela de Ciencias Aplicadas e Ingeniería, Universidad EAFIT, 050022 Medellín, Colombia.
Juan Felipe Restrepo-AriasEscuela de Ciencias Aplicadas e Ingeniería, Universidad EAFIT, 050022 Medellín, Colombia.
David VelásquezEscuela de Ciencias Aplicadas e Ingeniería, Universidad EAFIT, 050022 Medellín, Colombia.
John W Branch-BedoyaFacultad de Minas, Universidad Nacional de Colombia, 050041 Medellín, Colombia.
Mikel MaizaFundación Vicomtech, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, 20009 Donostia, San Sebastián, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Computer vision applications for detecting diseases in agriculture have been gaining relevance in recent years through the use of deep learning architectures. Digital image datasets serve as the main input for these architectures, enabling the analysis of patterns associated with a specific disease. However, some diseases have not yet been explored due to the limited availability of annotated image datasets. Cocoa pods are fundamental for the production of chocolate and its derived products; nevertheless, their production is threatened by

Indexed as

Cocoa diseaseCocoa disease detectionCocoa podImage acquisitionMonilia disease

Identifiers

PMID41608050
PMCPMC12834835

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.