Systematic mapping of plant detection and counting in agricultural images using machine learning – modeling proposal for system development

Authors

  • Carlos Daniel Pohlod Estudante de graduação no curso de Ciência de Computação da Universidade Estadual do Centro-Oeste, Guarapuava, PR.
  • Kelly Lais Wiggers Graduada em Ciência da Computação, doutora em Informática, professora no Departamento de Ciência da Computação da Universidade Estadual do Centro-Oeste, Guarapuava, PR.
  • Regiane Orlovski Graduada em Sistemas de Informação, doutoranda em Informática, professora no Departamento de Ciência da Computação da Universidade Estadual do Centro-Oeste, Guarapuava, PR.
  • Rodrigo Ferreira Engenheiro Agrônomo. Doutorando em Agronomia, pesquisador na Fundação Agrária de Pesquisa Agropecuária (Fapa), Guarapuava, PR.
  • Thais Amanda Santos Estudante de graduação no curso de Ciência de Computação da Universidade Estadual do Centro-Oeste, Guarapuava, PR.
  • William Nahirnei Lopes Estudante de graduação no curso de Ciência de Computação da Universidade Estadual do Centro-Oeste, Guarapuava, PR.

DOI:

https://doi.org/10.35977/0104-1096.cct2022.v39.26950

Keywords:

agriculture, production estimation, artificial neural networks, UAV

Abstract

The search for large-scale food production continues to be a global concern. In this regard, when detecting and counting plants, estimating production is an area that is explored by machine learning techniques. Given the above, this article aims to carry out a bibliographic mapping of machine learning approaches applied to plant detection and counting estimation. With this mapping, it was intended to evaluate if there are similarities between crops and techniques chosen by the authors and, in this way, to propose a model for future studies with images captured by UAVs. To achieve the proposed objective, a search string was applied to databases and the results were filtered. In this mapping, 18 papers were reported. The results showed that the state of the art indicates that Artificial Neural Network (ANN) models, mainly Convolutional Neural Networks (CNN), are being widely used in production counting/estimation.

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Published

2022-07-27

How to Cite

Pohlod, C. D., Wiggers, K. L., Orlovski, R., Ferreira, R., Santos, T. A., & Lopes, W. N. (2022). Systematic mapping of plant detection and counting in agricultural images using machine learning – modeling proposal for system development. Science & Technology Journals, 39(2), e26950. https://doi.org/10.35977/0104-1096.cct2022.v39.26950

Issue

Vol. 39 No. 2 (2022)

Section

Artigos

License

After publication of the work, the Authors are authorized to non-exclusively distribute the version of the published work (for example: publish in an institutional repository, as a book chapter, create links on electronic pages, etc.), as long as the authorship and initial publication in this journal are mentioned.