Evidence map›Paper›PMID 40648020›Full record

ArticlePlants (Basel, Switzerland)2025

Deep Learning Models for Detection and Severity Assessment of Cercospora Leaf Spot (

Douglas Vieira Leite, Alisson Vasconcelos de Brito, Gregorio Guirada Faccioli, Gustavo Haddad Souza Vieira

Abstract read
In one paragraph

Article in Plants (Basel, Switzerland), 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

4 authors.

Douglas Vieira LeiteEMEC, Sergipe Educational, Technology and Scientific Institute, Lourival Batista, Lourival Batista Highway s/n, Lagarto 49400-000, Sergipe, Brazil.ORCID 0000-0002-1585-061X
Alisson Vasconcelos de BritoLaboratory of Embedded Systems and Robotics, Paraíba Federal University, Campus I Lot. Cidade Universitaria, João Pessoa 58051-900, Paraıba, Brazil.ORCID 0000-0001-5215-443X
Gregorio Guirada FaccioliDevelopment and Environment Post-Graduation Program, Sergipe Federal University, Marcelo Déda Chagas Avenue, São Cristóvão 49107-230, Sergipe, Brazil.ORCID 0000-0003-2666-3606
Gustavo Haddad Souza VieiraPostgraduate Program in Agroecology, Federal Institute of Espírito Santo (IFES), Santa Teresa Campus, ES-080 Highway, Km 93, São João de Petrópolis, Santa Teresa 29660-000, Espírito Santo, Brazil.ORCID 0000-0001-9963-1571

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The accurate assessment of plant disease severity is crucial for effective crop management. Deep learning, especially via CNNs, is widely used for image segmentation in plant lesion detection, but accurately assessing disease severity across varied environmental conditions remains challenging. This study evaluates eight deep learning models for detecting and quantifying Cercospora leaf spot (

Indexed as

cercospora leaf spotCNNconvolutional neural networksplant disease severity

Identifiers

PMID40648020
PMCPMC12251717

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.