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dc.contributor.authorCESARO JÚNIOR, T. de
dc.contributor.authorOLIVEIRA, C. A. L. de
dc.contributor.authorLAU, D.
dc.contributor.authorRIEDER, R.
dc.date.accessioned2026-05-19T14:48:37Z-
dc.date.available2026-05-19T14:48:37Z-
dc.date.created2026-05-18
dc.date.issued2026
dc.identifier.citationNeotropical Entomology, v. 55, n. 1, 2026.
dc.identifier.issn1678-8052
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1186931-
dc.descriptionEffective pest management requires accurate and continuous monitoring. This monitoring helps assess population dynamics and guides the development of integrated pest management strategies. Traps used to capture insects are an alternative applied to various crops. However, the identification and manual counting of specimens are time-consuming, require taxonomic knowledge, and depend on the expertise of specialists. Automation could reduce costs, increase accuracy, and enable scalable analyses. Current computer vision and artificial intelligence techniques can quickly and accurately identify objects in digital images. This study presents a systematic review of literature retrieved from multidisciplinary and specialized databases (Scopus, ACM, Web of Science, IET, DBLP, Springer, and ScienceDirect), focusing on the intersections of agriculture, ecology, and computer science. We found 284 studies published between 2020 and 2025. Among them, 57 fulfilled the eligibility criteria, considering applied computing solutions for insect identification and counting using digital images of specimens collected via traps or photographed in situ on plants, in both field and laboratory settings. The findings highlight the use of electronic traps for real-time data collection and improvements in convolutional neural networks, with visual transformers and attention mechanisms for multi-species and fine-grained recognition. They also indicate opportunities to leverage microscopy resources, overcome limitations in the large-scale deployment and integration of electronic trap networks, and integrate real-time monitoring data with forecasting models using weather predictions to promote early warning systems for integrated pest management.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectElectronic trap
dc.titleFine-grained recognition of insect pests from digital images: a survey.
dc.typeArtigo de periódico
dc.subject.thesagroPraga de Planta
dc.subject.thesagroManejo
dc.subject.thesagroArmadilha
dc.subject.nalthesaurusIntegrated pest management
dc.subject.nalthesaurusComputer vision
dc.subject.nalthesaurusEntomology
dc.subject.nalthesaurusAgriculture
riaa.ainfo.id1186931
riaa.ainfo.lastupdate2026-05-19
dc.identifier.doihttps://doi.org/10.1007/s13744-026-01385-8
dc.contributor.institutionTELMO DE CESARO JÚNIOR, INSTITUTO FEDERAL DO RIO GRANDE DO SUL; CLAUDIO ANDRÉ LOPES DE OLIVEIRA, INSTITUTO FEDERAL DO RIO GRANDE DO SUL; DOUGLAS LAU, CNPF; RAFAEL RIEDER, UNIVERSIDADE DE PASSO FUNDO.
Aparece nas coleções:Artigo em periódico indexado (CNPF)

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