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dc.contributor.authorFURUYA, D. E. G.
dc.contributor.authorBOLFE, E. L.
dc.contributor.authorSILVEIRA, F. da
dc.contributor.authorBARBEDO, J. G. A.
dc.contributor.authorSILVA, T. L. da
dc.contributor.authorROMANI, L. A. S.
dc.contributor.authorCASTANHEIRO, L. F.
dc.contributor.authorGEBLER, L.
dc.date.accessioned2025-09-29T13:48:48Z-
dc.date.available2025-09-29T13:48:48Z-
dc.date.created2025-09-29
dc.date.issued2025
dc.identifier.citationClimate, v. 13, n. 10, 203, Oct. 2025.
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1179185-
dc.descriptionHailstorms are a major climatic threat to apple production, causing substantial economic losses in orchards worldwide. Anti-hail nets have been increasingly adopted to mitigate this risk, but the scientific literature on their effectiveness and future applications remains scattered, especially considering advances in digital agriculture. This study synthesizes current knowledge on the use of anti-hail nets in apple orchards through a systematic review and explores future perspectives involving digital technologies. A PRISMA-based review was conducted using three databases, revealing information regarding the studied countries, netting colors, and apple varieties, among others. A clear research gap was identified in integrating anti-hail nets with remote sensing and Artificial Intelligence (AI). This paper also analyzes studies from Vacaria, Brazil, a key apple-producing region and part of the Semear Digital project, highlighting local efforts to use hail netting in commercial orchards. Potential applications of AI algorithms and remote sensing are proposed for hail netting assessment, orchard monitoring, and decision-making support. These technologies can improve predictive modeling, quantify areas, and enhance precision management. Findings suggest combining traditional protective methods with technological innovations to strengthen orchard resilience in regions exposed to extreme weather.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectPomar de maçã
dc.subjectPRISMA
dc.subjectClima extremo
dc.subjectAprendizado de máquina
dc.subjectAprendizado profundo
dc.subjectAgricultura digital
dc.subjectExtreme weather
dc.subjectDeep learning
dc.subjectMachine learning
dc.subjectDigital agriculture
dc.titleHail netting in apple orchards: current knowledge, research gaps, and perspectives for digital agriculture.
dc.typeArtigo de periódico
dc.subject.thesagroMalus Domestica
dc.subject.thesagroSensoriamento Remoto
dc.subject.thesagroMaçã
dc.subject.nalthesaurusClimate
dc.subject.nalthesaurusRemote sensing
riaa.ainfo.id1179185
riaa.ainfo.lastupdate2025-09-29
dc.identifier.doihttps://doi.org/10.3390/cli13100203
dc.contributor.institutionDANIELLE ELIS GARCIA FURUYA; EDSON LUIS BOLFE, CNPTIA; FRANCO DA SILVEIRA; JAYME GARCIA ARNAL BARBEDO, CNPTIA; TAMIRES LIMA DA SILVA, UNIVERSIDADE ESTADUAL PAULISTA "JÚLIO DE MESQUITA FILHO"; LUCIANA ALVIM SANTOS ROMANI, CNPTIA; LETÍCIA FERRARI CASTANHEIRO; LUCIANO GEBLER, CNPUV.
Appears in Collections:Artigo em periódico indexado (CNPTIA)

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