Combination of remote sensing and artificial intelligence in fruit growing: progress, challenges, and potential applications.

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dc.contributorDANIELLE 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.
dc.creatorFURUYA, D. E. G.
dc.creatorBOLFE, E. L.
dc.creatorPARREIRAS, T. C.
dc.creatorBARBEDO, J. G. A.
dc.creatorSANTOS, T. T.
dc.creatorGEBLER, L.
dc.date2024-12-28T18:04:51Z
dc.date2024-12-28T18:04:51Z
dc.date2024-12-27
dc.date2024
dc.date.accessioned2026-07-07T04:17:59Z
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.identifierRemote Sensing, v.1 6, n. 24, 4805, Dec. 2024.
dc.identifier2072-4292
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1170914
dc.identifierhttps://doi.org/10.3390/rs16244805
dc.identifier.urihttp://hdl.handle.net/123456789/456958
dc.languageeng
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.subjectMaçã
dc.subjectUva
dc.subjectPomar
dc.subjectSensoriamento Remoto
dc.titleCombination of remote sensing and artificial intelligence in fruit growing: progress, challenges, and potential applications.
dc.typeArtigo de periódico

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