Evidence map›Paper›PMID 42776435›Full record

ArticleNeotropical entomology2026

Brazilian Insect Survey: A Platform for Pest Management.

Telmo De Cesaro Júnior, Bárbara Stella Wehrmann, Alexandre Tagliari Lazzaretti, Roberto Wiest, Jorge Luis Boeira Bavaresco, Brenda Slongo Taca, Nicolas Welfer Kirinus, Crislaine Sartori Suzana-Milan, Jayme Garcia Arnal Barbedo, Douglas Lau and 1 more

Abstract read
In one paragraph

Article in Neotropical entomology, 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

11 authors.

Telmo De Cesaro JúniorFederal Institute of Education, Science and Technology Sul-rio-grandense (IFSul), Passo Fundo, RS, Brazil.ORCID https://orcid.org/0000-0002-2886-229X
Bárbara Stella WehrmannFederal Institute of Education, Science and Technology Sul-rio-grandense (IFSul), Passo Fundo, RS, Brazil.ORCID https://orcid.org/0009-0005-0115-8664
Alexandre Tagliari LazzarettiFederal Institute of Education, Science and Technology Sul-rio-grandense (IFSul), Passo Fundo, RS, Brazil.ORCID https://orcid.org/0009-0004-8345-8216
Roberto WiestFederal Institute of Education, Science and Technology Sul-rio-grandense (IFSul), Passo Fundo, RS, Brazil.ORCID https://orcid.org/0000-0001-7982-6603
Jorge Luis Boeira BavarescoFederal Institute of Education, Science and Technology Sul-rio-grandense (IFSul), Passo Fundo, RS, Brazil.ORCID https://orcid.org/0009-0006-9691-2507
Brenda Slongo TacaUniversity of Passo Fundo (UPF), Passo Fundo, RS, Brazil.ORCID https://orcid.org/0009-0000-6408-5523
Nicolas Welfer KirinusUniversity of Passo Fundo (UPF), Passo Fundo, RS, Brazil.ORCID https://orcid.org/0009-0003-5260-3161
Crislaine Sartori Suzana-MilanUniversity of Passo Fundo (UPF), Passo Fundo, RS, Brazil.ORCID https://orcid.org/0000-0003-0562-7286
Jayme Garcia Arnal BarbedoBrazilian Agricultural Research Corporation (Embrapa Agricultura Digital), Campinas, SP, Brazil.ORCID https://orcid.org/0000-0002-1156-8270
Douglas LauBrazilian Agricultural Research Corporation (Embrapa Florestas), Colombo, PR, Brazil.ORCID https://orcid.org/0000-0001-8648-0102
Rafael RiederUniversity of Passo Fundo (UPF), Passo Fundo, RS, Brazil. rieder@upf.br.ORCID https://orcid.org/0000-0002-7435-9054

Funding

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior Finance Code 001
6 · The paper itself

Abstract

Modern agriculture faces significant challenges in integrated pest management, where collecting, connecting, and processing monitoring data in real time are essential. This study presents the Brazilian Insect Survey (BIS), a web-based platform designed to centralize and streamline phytosanitary data management, enabling the integration of computer vision, field experimentation, and population modeling within a unified digital ecosystem. The platform is organized into functional modules that support experimental data management (TrapSystem and AgroExperiment), automated insect detection and counting from digital images (InsectCV and AphidCV), and simulation of aphid population dynamics (ABISM). These components operate in synergy, supported by distributed processing infrastructure, to ensure scalable data handling and efficient analytical workflows. Multi-year field applications demonstrate that integrating field data acquisition and automated image analysis with mechanistic population modeling enables timely, model-driven interventions that reduce aphid infestation levels and protect crop yield potential under variable environmental conditions, providing empirical evidence for queryPlease check if the captured keywords are correct.the effectiveness of combining AI-based insect detection with population dynamics modeling in operational integrated pest management. The case studies presented here demonstrate that the BIS platform successfully integrates computer vision, field experimentation, and population modeling within a modular digital ecosystem, highlighting its potential to enhance decision-making and advance data-driven integrated pest management.

Indexed as

InsectaInsect ControlAgricultureAnimalsAphidsBrazilPopulation DynamicsIntegrated pest management, Computer vision, Entomological monitoring, Precision agriculture, Agricultural decision support systems

Identifiers

PMID42776435
PMCPMC13601172

What OpenQuestion holds

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Registered trials

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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.