Big data technology applications in agriculture: a systematic literature review

Authors

DOI:

https://doi.org/10.5585/exactaep.2021.17765

Keywords:

Big Data, Agriculture, Industry 4.0.

Abstract

Industry 4.0 is a terminology widely used today. Among the technologies that make up this new trend, there is Big Data, which is a broad set of data with a large number of variables, high number and high speed. The objective of this article was to carry out a systematic review of the literature regarding the current issues that address the use of Big Data in the context of Agriculture. The systematic literature review was able to verify how this sector analyzes and processes the large volume of data generated. Thus, there was a search for articles published on the Web of Science and Scopus in the intervals between 2016 and 2019, which contained as Big Data and agriculture. The material found was analyzed, compiled and presented in the form of a table with a short summary on what to approach the articles. As a result, it was observed that a large part of the studies refer to the use of analysis and machine learning techniques of data sets from Big Data, which propose solutions to problems arising from agriculture. In addition, this study serves as a reference on the Big Data techniques most used in agriculture aiming at increasing productivity and better decision making.

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Author Biographies

Thiago Shoji Obi Tamachiro, Universidade Federal do Paraná – UFPR

Mestrando em Engenharia de Produção, linha de pesquisa: inovação em projetos, produtos e processos, pela Universidade Federal do Paraná. Especializando em Gestão de Operações - Six Sigma Black Belt pela FAE Business School. Graduação em Engenharia de Produção pela FAE Centro Universitário (2018).

Fernanda Robes de Oliveira, Universidade Federal do Paraná – UFPR

Possui graduação em Estatística pela Universidade Federal do Paraná (2010). Pós graduação em Marketing pela FAE (2015). Gestora da atividade de pesquisa no SEBRAE/PR, atua como consultora no desenvolvimento de estudos e pesquisas para instituição com interação com as áreas de pesquisa de outros SEBRAE/UFs, além de parceiros como FIEP e Fecomércio. Desenvolvimento de metodologias de coleta de indicadores de resultado. Monitoramento dos indicadores institucionais de Imagem, Satisfação e NPS. Conhecimento de ferramentas de BI QlikView (Desenvolvimento e análise) e Modelagem de Processos (Bizagi).

Jéssika Alvares Coppi Arruda Gayer, Universidade Federal do Paraná – UFPR

Mestranda em Engenharia de Produção pela Universidade Federal do Paraná - UFPR (2019). Professora orientadora dos TCCs (artigo) da Pós-Graduação em Engenharia de Produção - UNINTER (2018 - 2019). Membro do Grupo de Pesquisa de Gestão em Inovação e Sustentabilidade da UNINTER (2017). Tutora/Professora do Curso Bacharel em Engenharia de Produção EaD e Presencial - Uninter (2015). Pós-Graduanda em Formação de Docentes para EAD pela UNINTER (2019). Especialista em Formação de Tutores na Modalidade de Educação a Distância pela Faculdade Bagozzi (2017). Graduada em Engenharia de Produção pela Universidade Positivo (2015). Possui formação no Curso Superior Sequencial em Química Ambiental Aplicada à Indústria pela Pontifícia Universidade Católica do Paraná (2009).

Mariana Kleina, Universidade Federal do Paraná – UFPR

Professora Adjunta do Departamento de Engenharia de Produção da Universidade Federal do Paraná desde 2016. Possui graduação em Matemática Industrial (2009), mestrado (2012) e doutorado (2015) em Métodos Numéricos em Engenharia pela Universidade Federal do Paraná. Tem interesse nas áreas de Pesquisa Operacional e Inteligência Artificial.

Marcos Augusto Mendes Marques, Universidade Federal do Paraná – UFPR

Possui graduação em Engenharia Elétrica pela Universidade Federal do Paraná (2003) e doutorado em Métodos Numéricos em Engenharia pela Universidade Federal do Paraná (2015). Atualmente é professor adjunto do Departamento de Engenharia de Produção da Universidade Federal do Paraná. Tem experiência na área de Matemática e Estatística, com ênfase em Análise Numérica e Simulação, atuando principalmente nos seguintes temas: otimização, métodos estatísticos, regressão linear múltipla, descarte de variáveis, simulação, engenharia da qualidade e energia elétrica. Atualmente leciona as disciplinas de Engenharia da Qualidade, Manutenção e Confiabilidade, Sistemas de Medição e Metrologia, para o Departamento de Engenharia de Produção do Setor de Tecnologia da Universidade Federal do Paraná.

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Published

2022-04-04

How to Cite

Tamachiro, T. S. O., Oliveira, F. R. de, Gayer, J. A. C. A., Kleina, M., & Marques, M. A. M. (2022). Big data technology applications in agriculture: a systematic literature review. Exacta, 20(2), 388–402. https://doi.org/10.5585/exactaep.2021.17765

Issue

Vol. 20 No. 2 (2022): (abr./jun.)

Section

Papers

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