Evidence map›Paper›PMID 42491445›Full record

ReviewPlant phenomics (Washington, D.C.)2026

Harnessing high-throughput phenotyping and artificial intelligence for soybean breeding: From trait assessment to data-driven decisions.

José T B Chagas, Maurício S Araújo, João P S Pavan, André O Françani, Gérson N C Ferreira, André A Stella, Marcela Martinez, Yasmim I Retore, Bruno F Fregonezi, Gyanlucca M Martins and 10 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Plant phenomics (Washington, D.C.), 2026. 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

20 authors.

José T B ChagasGenetics Diversity and Breeding Laboratory, Department of Genetics, University of São Paulo, Av. Pádua Dias, 11, Piracicaba, SP, 13418-900, Brazil.
Maurício S AraújoGenetics Diversity and Breeding Laboratory, Department of Genetics, University of São Paulo, Av. Pádua Dias, 11, Piracicaba, SP, 13418-900, Brazil.
João P S PavanGenetics Diversity and Breeding Laboratory, Department of Genetics, University of São Paulo, Av. Pádua Dias, 11, Piracicaba, SP, 13418-900, Brazil.
André O FrançaniFaculty of Philosophy, Sciences and Letters at Ribeirão Preto (FFCLRP), University of São Paulo, Av. Bandeirantes, 3900, Ribeirão Preto, SP, 14040-901, Brazil.
Gérson N C FerreiraGenetics Diversity and Breeding Laboratory, Department of Genetics, University of São Paulo, Av. Pádua Dias, 11, Piracicaba, SP, 13418-900, Brazil.
André A StellaGenetics Diversity and Breeding Laboratory, Department of Genetics, University of São Paulo, Av. Pádua Dias, 11, Piracicaba, SP, 13418-900, Brazil.
Marcela MartinezGenetics Diversity and Breeding Laboratory, Department of Genetics, University of São Paulo, Av. Pádua Dias, 11, Piracicaba, SP, 13418-900, Brazil.
Yasmim I RetoreGenetics Diversity and Breeding Laboratory, Department of Genetics, University of São Paulo, Av. Pádua Dias, 11, Piracicaba, SP, 13418-900, Brazil.
Bruno F FregoneziGenetics Diversity and Breeding Laboratory, Department of Genetics, University of São Paulo, Av. Pádua Dias, 11, Piracicaba, SP, 13418-900, Brazil.
Gyanlucca M MartinsGenetics Diversity and Breeding Laboratory, Department of Genetics, University of São Paulo, Av. Pádua Dias, 11, Piracicaba, SP, 13418-900, Brazil.
Tong ZhaoGenetics Diversity and Breeding Laboratory, Department of Genetics, University of São Paulo, Av. Pádua Dias, 11, Piracicaba, SP, 13418-900, Brazil.
Jianye ZhouGenetics Diversity and Breeding Laboratory, Department of Genetics, University of São Paulo, Av. Pádua Dias, 11, Piracicaba, SP, 13418-900, Brazil.
Dayana R SousaGenetics Diversity and Breeding Laboratory, Department of Genetics, University of São Paulo, Av. Pádua Dias, 11, Piracicaba, SP, 13418-900, Brazil.
Yuefang GaoCollege of Mathematics and Informatics, South China Agricultural University, Guangzhou, China.
Donghong JiSchool of Cyber Science and Engineering, Wuhan University, Wuhan, China.
Kaizhou GaoMacau Institute of System Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, 999078, Macao.
Bo LiYazhouwan National Laboratory, Sanya, China.
Shouyang LiuEngineering Research Center of Plant Phenotyping, Ministry of Education, State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University, Nanjing, 210095, China.
Liang ZhaoFaculty of Philosophy, Sciences and Letters at Ribeirão Preto (FFCLRP), University of São Paulo, Av. Bandeirantes, 3900, Ribeirão Preto, SP, 14040-901, Brazil.
José B PinheiroGenetics Diversity and Breeding Laboratory, Department of Genetics, University of São Paulo, Av. Pádua Dias, 11, Piracicaba, SP, 13418-900, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Soybean (

Indexed as

Computer visionDeep learningGlycine max (L.) MerrillMachine learningPhenomicsRemote sensing

Identifiers

PMID42491445
PMCPMC13377444

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.