MACHINE LEARNING ALGORITHMS IN AGRICULTURE: A LITERATURE REVIEW ON CLIMATE AND PRICE PREDICTION, PEST AND DISEASE DETECTION, AND PRODUCTION MONITORING

Autores

DOI:

https://doi.org/10.47820/recima21.v6i2.6211

Palavras-chave:

Agricultura, Algoritmo, Aprendizado de máquina, Floresta aleatória, IA, Previsão, Seca

Resumo

The demand for food is growing every year and demands more significant technology applications in the field Furthermore, due to food production, pests and climate change incidents are a real-time challenge for farmers. Due to the growing need to apply algorithms in the field, we investigate the algorithms most cited, used, and ongoing projects in the last three years, from 2019 to 2021 Therefore, we evaluated articles that focus was mainly on supervised learning algorithms This literature review presents an overview of algorithms usage in agriculture. A total of 81 articles were analysed. Our contributions as a) an analysis of the state-of-the-art on applying algorithms to various agricultural functions and b) a taxonomy to help researchers, governments, and farmers choose these algorithms. This article adds discoveries about the application of algorithms in crops, machinery, and processes and points out new lines of research.

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Biografia do Autor

  • Emiliano Soares Monteiro

    UNEMAT

  • Rodrigo da Rosa Righi

    Professor and researcher at University of Vale do Rio dos Sinos (Unisinos), Brazil. Post-doctoral studies at KAIST - Korean Advanced Institute of Science and Technology, South Korea, under the following topics: RFID and cloud computing. MS and PhD degrees in Computer Science from the Federal University of Rio Grande do Sul, Brazil. Performed a stage at Tecnische Universitaet Berlin, Germany. Member of both IEEE Computer Society and ACM.

  • Antônio Marcos Alberti

    Associate Professor in Software Engineering at the School of Computer Science, University of Leeds, UK. PhD at the State University of Campinas (Unicamp), Brazil, Researcher at the Electronics and Telecommunications Research Institute (ETRI), South Korea. 

  • Sandro José Rigo

    Professor/Researcher in the Applied Computing Graduate Program Program – UNISINOS; Head of the Undergraduate Course in Computer Science in UNISINOS; Head of the Graduate Degree Program in Applied Computer Science in UNISINOS; Dean of the UNISINOS Polytechnic School. BSc in Computer Science at Pontifícia Universidade Católica do Rio Grande do Sul PUCRS; MSc in Computer Science at Universidade Federal do Rio Grande do Sul UFRGS; Ph.D. in Computer Science at Universidade Federal do Rio Grande do Sul UFRGS. Professor and Researcher at UNISINOS University. Scholarship for Productivity in Technological Development and Innovative Extension (DT-2) of the National Council for Scientific and Technological Development (CNPq). Main research areas: Artificial Intelligence, Semantic Web, Natural Language Processing, Robotic, Distance Education. Doctoral thesis in progress: 2; Software registered (INPI – Instituto Nacional de Propriedade Intelectual / National Institute of Intelectual Property).

  • Jorge Luis Victoria Barbosa

    Graduação em Tecnologia em Processamento de Dados e Engenharia Elétrica pela Universidade Católica de Pelotas (UCPel). Especialização em Engenharia de Software (UCPel). Mestrado e doutorado em ciência da computação na Universidade Federal do Rio Grande do Sul (UFRGS). Pós-doutorado na Sungkyunkwan University (SKKU, Suwon, Coréia do Sul). Pós-doutorado na University of California Irvine (UCI, Irvine, USA) através de uma bolsa do Programa CAPES/PRINT (professor visitante no Exterior Sênior). Professor Titular II na Universidade do Vale do Rio dos Sinos (Unisinos). Professor no Programa de Pós-graduação em Computação Aplicada (PPGCA) e no Programa de Pós-Graduação em Engenharia Elétrica (PPGEL). Coordenador do Laboratório de Computação Móvel (Mobilab) e atua como Bolsista de Produtividade em Desenvolvimento Tecnológico e Extensão Inovadora (bolsa DT - atualmente no Nível 1C) do Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq). 

  • Perla Haydee da Silva

    PhD in Linguistic Studies from the Institute of Languages at UFMT (PPGEL). Holds a Master’s degree in Applied Linguistics from the Institute of Languages at UFMT (PPGEL), a specialization in Higher Education Didactics from UNIC - Universidade de Cuiabá, a Bachelor’s degree in Law from UNIC - Universidade de Cuiabá, and a Bachelor’s degree in Language and Literature from UFMT - Universidade Federal de Mato Grosso.

    Worked as a professor at the Faculty of Engineering, UFMT, Várzea Grande University Campus, where she taught the subjects Communication, Expression, and Technical Writing, as well as a Scientific Initiation Workshop, from 2014 to 2021.

    Currently, she is the coordinator of the Undergraduate Program in English Language and Literature at UFMT (since 2022) and teaches the subject English Language 2.

    Her research interests include the following areas: Applied Linguistics in Foreign Language Teaching, focusing on themes such as textbooks, English language teaching and learning, and teacher training; and Discourse Analysis with an emphasis on Gender Theories and Feminism.

  • Lidia Martins da Silva

    Doutorado em Computação Aplicada pela Universidade do Vale do Rio dos Sinos Unisinos, Mestrado em Ciência da Computação, Especialização em Formação Tecnológica e Inclusiva, Especialização em Informática na Educação, Especialização em Design Instrucional, Graduação em TPD - Tecnologia em Processamento de Dados. Coordenadora dos Laboratórios de Informática e do Núcleo de Processamento de Dados do Centro Universitário Cândido Rondon, Professora do Curso Ciência da Computação e Tecnologia em Análise de Sistemas do Centro Universitário Cândido Rondon. Tutora presencial na Centro Universitário Claretiano, Tutora no IFMT, professora e tutora nos cursos a distância da UFMT, Professora no curso de Pós Graduação Governança de TI do SENAC- MT, professora no curso de Sistemas da Informação no UNIVAG, Coordenadora do Núcleo de Execução dos cursos EaD - Projeto TCE e Universidade Federal de Mato Grosso - SETEC.  Coordenadora dos Cursos: Tecnologia em Análise e Desenvolvimento de Sistemas e Bacharelado em Ciência da Computação na Faculdade INVEST de Ciências e Tecnologia.

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MACHINE LEARNING ALGORITHMS IN AGRICULTURE: A LITERATURE REVIEW ON CLIMATE AND PRICE PREDICTION, PEST AND DISEASE DETECTION, AND PRODUCTION MONITORING. (2025). RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218, 6(2), e626211. https://doi.org/10.47820/recima21.v6i2.6211