Please use this identifier to cite or link to this item: http://www.alice.cnptia.embrapa.br/alice/handle/doc/1170914
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dc.contributor.authorFURUYA, D. E. G.
dc.contributor.authorBOLFE, E. L.
dc.contributor.authorPARREIRAS, T. C.
dc.contributor.authorBARBEDO, J. G. A.
dc.contributor.authorSANTOS, T. T.
dc.contributor.authorGEBLER, L.
dc.date.accessioned2024-12-28T18:04:51Z-
dc.date.available2024-12-28T18:04:51Z-
dc.date.created2024-12-27
dc.date.issued2024
dc.identifier.citationRemote Sensing, v.1 6, n. 24, 4805, Dec. 2024.
dc.identifier.issn2072-4292
dc.identifier.urihttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1170914-
dc.descriptionFruit growing is important in the global agricultural economy, contributing significantly to food security, job creation, and rural development. With the advancement of technologies, mapping fruits using remote sensing and machine learning (ML) and deep learning (DL) techniques has become an essential tool to optimize production, monitor crop health, and predict harvests with greater accuracy. This study was developed in four main stages. In the first stage, a comprehensive review of the existing literature was made from July 2018 (first article found) to June 2024, totaling 117 articles. In the second stage, a general analysis of the data obtained was made, such as the identification of the most studied fruits with the techniques of interest. In the third stage, a more in-depth analysis was made focusing on apples and grapes, with 27 and 30 articles, respectively. The analysis included the use of remote sensing (orbital and proximal) imagery and ML/DL algorithms to map crop areas, detect diseases, and monitor crop development, among other analyses. The fourth stage shows the data’s potential application in a Southern Brazilian region, known for apple and grape production. This study demonstrates how the integration of modern technologies can transform fruit farming, promoting more sustainable and efficient agriculture through remote sensing and artificial intelligence technologies.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectAgricultura digital
dc.subjectAprendizado profundo
dc.subjectAprendizado de máquina
dc.subjectInteligência artificial
dc.subjectDigital agriculture
dc.subjectDeep learning
dc.subjectMachine learning
dc.subjectOrchard
dc.subjectApple
dc.subjectGrape
dc.titleCombination of remote sensing and artificial intelligence in fruit growing: progress, challenges, and potential applications.
dc.typeArtigo de periódico
dc.subject.thesagroMaçã
dc.subject.thesagroUva
dc.subject.thesagroPomar
dc.subject.thesagroSensoriamento Remoto
riaa.ainfo.id1170914
riaa.ainfo.lastupdate2024-12-27
dc.identifier.doihttps://doi.org/10.3390/rs16244805
dc.contributor.institutionDANIELLE ELIS GARCIA FURUYA; EDSON LUIS BOLFE, CNPTIA, UNIVERSIDADE ESTADUAL DE CAMPINAS; TAYA CRISTO PARREIRAS, UNIVERSIDADE ESTADUAL DE CAMPINAS; JAYME GARCIA ARNAL BARBEDO, CNPTIA; THIAGO TEIXEIRA SANTOS, CNPTIA; LUCIANO GEBLER, CNPUV.
Appears in Collections:Artigo em periódico indexado (CNPTIA)

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