Combining Artificial Intelligence and Satellite Images to Forecast Agricultural Losses: Evidence for Brazil

Detalhes bibliográficos
Autor(a) principal: Batista de Barros, Pedro Henrique
Data de Publicação: 2023
Outros Autores: Freitas Junior, Adirson Maciel de
Tipo de documento: Artigo
Idioma: por
Título da fonte: Revista Brasileira de Economia (Online)
Texto Completo: https://periodicos.fgv.br/rbe/article/view/84823
Resumo: The agricultural sector is subject to adversities arising from weather events, incidence of pests, fires and market variations, therefore, it is extremely important to adopt rural insurance for an adequate management of agricultural activities. However, the existence of market failures inhibits the development and expansion of this market, especially in Brazil. In this context, the main goal of this article is to propose an innovative methodology that combines machine learning algorithms with optical and radar satellite images for forecasting agricultural losses, thus allowing for the reduction of informational asymmetries in the Brazilian market.
id FGV-8_381890fc9195a7e147109491a66130e0
oai_identifier_str oai:ojs.periodicos.fgv.br:article/84823
network_acronym_str FGV-8
network_name_str Revista Brasileira de Economia (Online)
repository_id_str
spelling Combining Artificial Intelligence and Satellite Images to Forecast Agricultural Losses: Evidence for BrazilCombinando Inteligência Artificial e imagens de satélite para a previsão de sinistros agrícolas: Uma nota: Artificial IntelligenceMachine LearningSatellite ImagesRemote sensing;Rural InsuranceArtificial IntelligenceRemote SensingRural InsurenceAgricultural LossesInteligência ArtificialMachine LearningImagens de SatéliteSensoriamento RemotoSeguro RuralInteligência ArtificialSensoriamento RemotoSeguro RuralSinistro AgrícolaThe agricultural sector is subject to adversities arising from weather events, incidence of pests, fires and market variations, therefore, it is extremely important to adopt rural insurance for an adequate management of agricultural activities. However, the existence of market failures inhibits the development and expansion of this market, especially in Brazil. In this context, the main goal of this article is to propose an innovative methodology that combines machine learning algorithms with optical and radar satellite images for forecasting agricultural losses, thus allowing for the reduction of informational asymmetries in the Brazilian market.O setor agrícola está sujeito a adversidades provenientes de eventos climáticos, incidência de pragas, incêndios e variações de mercado, sendo, portanto, de suma importância a adoção de seguro rural para uma gestão adequada das atividades agrícolas. Entretanto, a existência de falhas de mercado inibe o desenvolvimento e a ampliação desse mercado, especialmente no Brasil. Nesse contexto, o principal objetivo desse artigo é propor uma metodologia inovadora que combina algoritmos de aprendizado de máquina com imagens de satélite ópticas e de radar para previsão de sinistros agrícolas que permita uma redução das assimetrias informacionais existentes no mercado brasileiro.  EGV EPGE2023-04-03info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticlesArtigosTextoinfo:eu-repo/semantics/otherapplication/pdfhttps://periodicos.fgv.br/rbe/article/view/84823Revista Brasileira de Economia; Vol. 77 No. 1 (2023): JAN - MARRevista Brasileira de Economia; v. 77 n. 1 (2023): JAN - MAR1806-91340034-7140reponame:Revista Brasileira de Economia (Online)instname:Fundação Getulio Vargas (FGV)instacron:FGVporhttps://periodicos.fgv.br/rbe/article/view/84823/83771Brazil, Paraná, 2020.Brasil, Paraná, 2020.Copyright (c) 2023 Revista Brasileira de Economiainfo:eu-repo/semantics/openAccessBatista de Barros, Pedro HenriqueFreitas Junior, Adirson Maciel de2023-04-03T19:33:14Zoai:ojs.periodicos.fgv.br:article/84823Revistahttps://periodicos.fgv.br/rbe/https://periodicos.fgv.br/rbe/oai||rbe@fgv.br1806-91340034-7140opendoar:2024-03-06T13:03:53.349959Revista Brasileira de Economia (Online) - Fundação Getulio Vargas (FGV)true
dc.title.none.fl_str_mv Combining Artificial Intelligence and Satellite Images to Forecast Agricultural Losses: Evidence for Brazil
Combinando Inteligência Artificial e imagens de satélite para a previsão de sinistros agrícolas: Uma nota:
title Combining Artificial Intelligence and Satellite Images to Forecast Agricultural Losses: Evidence for Brazil
spellingShingle Combining Artificial Intelligence and Satellite Images to Forecast Agricultural Losses: Evidence for Brazil
Batista de Barros, Pedro Henrique
Artificial Intelligence
Machine Learning
Satellite Images
Remote sensing;
Rural Insurance
Artificial Intelligence
Remote Sensing
Rural Insurence
Agricultural Losses
Inteligência Artificial
Machine Learning
Imagens de Satélite
Sensoriamento Remoto
Seguro Rural
Inteligência Artificial
Sensoriamento Remoto
Seguro Rural
Sinistro Agrícola
title_short Combining Artificial Intelligence and Satellite Images to Forecast Agricultural Losses: Evidence for Brazil
title_full Combining Artificial Intelligence and Satellite Images to Forecast Agricultural Losses: Evidence for Brazil
title_fullStr Combining Artificial Intelligence and Satellite Images to Forecast Agricultural Losses: Evidence for Brazil
title_full_unstemmed Combining Artificial Intelligence and Satellite Images to Forecast Agricultural Losses: Evidence for Brazil
title_sort Combining Artificial Intelligence and Satellite Images to Forecast Agricultural Losses: Evidence for Brazil
author Batista de Barros, Pedro Henrique
author_facet Batista de Barros, Pedro Henrique
Freitas Junior, Adirson Maciel de
author_role author
author2 Freitas Junior, Adirson Maciel de
author2_role author
dc.contributor.author.fl_str_mv Batista de Barros, Pedro Henrique
Freitas Junior, Adirson Maciel de
dc.subject.por.fl_str_mv Artificial Intelligence
Machine Learning
Satellite Images
Remote sensing;
Rural Insurance
Artificial Intelligence
Remote Sensing
Rural Insurence
Agricultural Losses
Inteligência Artificial
Machine Learning
Imagens de Satélite
Sensoriamento Remoto
Seguro Rural
Inteligência Artificial
Sensoriamento Remoto
Seguro Rural
Sinistro Agrícola
topic Artificial Intelligence
Machine Learning
Satellite Images
Remote sensing;
Rural Insurance
Artificial Intelligence
Remote Sensing
Rural Insurence
Agricultural Losses
Inteligência Artificial
Machine Learning
Imagens de Satélite
Sensoriamento Remoto
Seguro Rural
Inteligência Artificial
Sensoriamento Remoto
Seguro Rural
Sinistro Agrícola
description The agricultural sector is subject to adversities arising from weather events, incidence of pests, fires and market variations, therefore, it is extremely important to adopt rural insurance for an adequate management of agricultural activities. However, the existence of market failures inhibits the development and expansion of this market, especially in Brazil. In this context, the main goal of this article is to propose an innovative methodology that combines machine learning algorithms with optical and radar satellite images for forecasting agricultural losses, thus allowing for the reduction of informational asymmetries in the Brazilian market.
publishDate 2023
dc.date.none.fl_str_mv 2023-04-03
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articles
Artigos
Texto
info:eu-repo/semantics/other
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://periodicos.fgv.br/rbe/article/view/84823
url https://periodicos.fgv.br/rbe/article/view/84823
dc.language.iso.fl_str_mv por
language por
dc.relation.none.fl_str_mv https://periodicos.fgv.br/rbe/article/view/84823/83771
dc.rights.driver.fl_str_mv Copyright (c) 2023 Revista Brasileira de Economia
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2023 Revista Brasileira de Economia
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.coverage.none.fl_str_mv Brazil, Paraná, 2020.
Brasil, Paraná, 2020.
dc.publisher.none.fl_str_mv EGV EPGE
publisher.none.fl_str_mv EGV EPGE
dc.source.none.fl_str_mv Revista Brasileira de Economia; Vol. 77 No. 1 (2023): JAN - MAR
Revista Brasileira de Economia; v. 77 n. 1 (2023): JAN - MAR
1806-9134
0034-7140
reponame:Revista Brasileira de Economia (Online)
instname:Fundação Getulio Vargas (FGV)
instacron:FGV
instname_str Fundação Getulio Vargas (FGV)
instacron_str FGV
institution FGV
reponame_str Revista Brasileira de Economia (Online)
collection Revista Brasileira de Economia (Online)
repository.name.fl_str_mv Revista Brasileira de Economia (Online) - Fundação Getulio Vargas (FGV)
repository.mail.fl_str_mv ||rbe@fgv.br
_version_ 1798943115709513728

Registros relacionados