Radial base neural network for the detection of banana maturation stages: perceptron multilayer network comparison

Alfredo Bonini Neto; Vitória Ferreira da Silva Fávaro; Wesley Prado Leão dos Santos; Jéssica Marques de Mello; Angela Vacaro de Souza

doi:10.18011/bioeng.2022.v16.1175

Autores

Alfredo Bonini Neto Department of Biosystems Engineering, School of Science and Engineering, São Paulo State University - UNESP, Tupã, SP, Brazil. https://orcid.org/0000-0002-0250-489X
Vitória Ferreira da Silva Fávaro UNESP https://orcid.org/0000-0003-0688-3628
Wesley Prado Leão dos Santos Department of Biosystems Engineering, School of Science and Engineering, São Paulo State University - UNESP, Tupã, SP, Brazil.
Jéssica Marques de Mello Department of Biosystems Engineering, School of Science and Engineering, São Paulo State University - UNESP, Tupã, SP, Brazil.
Angela Vacaro de Souza Department of Biosystems Engineering, School of Science and Engineering, São Paulo State University - UNESP, Tupã, SP, Brazil. https://orcid.org/0000-0002-4647-2391

DOI:

https://doi.org/10.18011/bioeng.2022.v16.1175

Palavras-chave:

Radial base, Maturation stages, Multilayer Perceptron, Musa acuminata, Artificial neural networks

Resumo

Agriculture is one of the pillars of human existence since it allows for the obtention of food as well as other products for food production processes. In this regard, there are some crops, such as climactic fruits, that face difficulties especially regarding classification of their maturation stages at the time of harvest, which is the case of bananas, the focus of this work. Therefore, there are some techniques that use artificial neural networks to classify them, such as multilayer networks. Examples of such networks are Perceptron, widely used in several areas, and Radial Base Functional networks (RBF), whose studies are incipient and have little use in agricultural areas. Hence, the objective of the present work was to carry out a comparison between these two neural networks to verify which provides the highest accuracy. In this work it was possible to verify that radial base functional neural networks provide a faster and more efficient categorization for the stages of bananas maturation, because they do not require training and, therefore, have low computational cost, saving more energy, when compared to a Multilayer Perceptron. Therefore, it can be inferred that Radial Base Functional Artificial Neural Networks (RBF ANN) can be widely used in agriculture, enabling the improvement of different cultures and different processes, such as harvesting.

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Referências

Barbosa, V. V.; et al. (2018). Influência da variabilidade climática na produção de leite na região semiárida do nordeste do brasil. Revista Brasileira de Climatologia. Curitiba. http://dx.doi.org/10.5380/abclima.v23i0.58007. DOI: https://doi.org/10.5380/abclima.v23i0.58007

Barros. G.; et al. (2022). PIB do agro cresce 8,36% em 2021; participação no PIB brasileiro chega a 27,4%. Cepea-Esalq USP. Piracicaba. Disponível em: https://www.cepea.esalq.usp.br/br/releases/pib-agro-cepea-pib-do-agro-cresce-8-36-em-2021-participacao-no-pib-brasileiro-chega-a-27-4.aspx#:~:text=mandioca-,PIB%2DAgro%2FCEPEA%3A%20PIB%20do%20agro%20cresce%208%2C,brasileiro%20chega%20a%2027%2C4%25.

Bishop, C. M. (1995). Neural Networks for Pattern Recognition. Oxford University Press. DOI: https://doi.org/10.1201/9781420050646.ptb6

Bonini, A. N.; et al. (2021). Classification of banana ripening stages by artificial neural networks as a function of plant physical, physicochemical, and biochemical parameters. Engenharia Agrícola, [S.L.], v. 42, n. 3, p. 1-2, jun. 2022. FapUNIFESP (SciELO). http://dx.doi.org/10.1590/1809-4430-eng.agric.v42n3e20210197/2022. DOI: https://doi.org/10.1590/1809-4430-eng.agric.v42n3e20210197/2022

Bonini, A. N.; et al. (2021). Interface gráfica via redes neurais artificiais: um estudo de caso na estimativa da produção de capim marandu em sistemas integrados. Revista Brasileira de Engenharia de Biossistemas, [S.L.], 15 (2), 318-341. Universidade Estadual Paulista - Campus de Tupa. http://dx.doi.org/10.18011/bioeng2021v15n2p318-341. DOI: https://doi.org/10.18011/bioeng2021v15n2p318-341

Cerqueira, E. O.; et al. (2002). Redes neurais e suas aplicações em calibração multivariada. Sociedade Brasileira de Química. 10-10. https://doi.org/10.1590/S0100-40422001000600025. DOI: https://doi.org/10.1590/S0100-40422001000600025

Contreras, J.; Gracia, M. (2011). Alimentação, Sociedade e Cultura. Editora Fiocruz: Rio de Janeiro.

Haykin, S. (1994). Neural Networks: A Comprehensive Foundation. Macmillan Publishing, New York.

Haykin, S. (1999). Neural Networks: A Comprehensive Foundation. Prentice Hall.

ICMC (Instituto de Ciências Matemáticas e de Computação) USP. (2009). Perceptron Multi-Camadas (MLP). São Carlos. https://sites.icmc.usp.br/andre/research/neural/MLP.htm.

IPEA & ONU (Organização das Nações Unidas). (2019). Objetivos de Desenvolvimento Sustentável. https://www.ipea.gov.br/ods/ods14.html.

Jomori, M. M.; et al. (2008). Determinantes de escolha alimentar. Brazilian Journal of Nutrition. Campinas, 21 (1), 63-73. https://www.scielo.br/j/rn/a/kgXSTmYNgrjFQjC DOI: https://doi.org/10.1590/S1415-52732008000100007

Mathworks. (2022). Disponível em: http://www.mathworks.com.

Ministério da Saúde; et al. (2014). Guia Alimentar para a População Brasileira. 2 ed: Brasília.

Ministério do meio ambiente. (2022). Biodiversidade Brasileira. Brasília. https://antigo.mma.gov.br/biodiversidade/biodiversidade-brasileira.html.

Minussi C. R., Lotufo A. D. P. (2008). Redes Neurais: Introdução e Principais Conceitos. Faculdade de Ilha Solteira. Notas de aula da disciplina Redes Neurais Artificiais – UNESP. https://www.feis.unesp.br/#!/departamentos/engenharia-eletrica/pos-graduacao/disciplinas4211/rna/.

ONU (Organização das Nações Unidas). (2019). Objetivos de Desenvolvimento Sustentável. https://brasil.un.org/pt-br/sdgs.

Putti, F. F. et al. (2017). A Fuzzy mathematical model to estimate the effects of global warming on the vitality of Laelia purpurata orchids. Mathematical Biosciences, Tupã, 288, 124-129. http://dx.doi.org/10.1016/j.mbs.2017.03.005. DOI: https://doi.org/10.1016/j.mbs.2017.03.005

Shiratsuchi, L. S; Embrapa et al. (2014). Sensoriamento remoto: conceitos básicos e aplicações na agricultura de precisão. Brasília. https://www.embrapa.br/busca-de-publicacoes/-/publicacao/1003275/sensoriamento-remoto-conceitos-basicos-e-aplicacoes-na-agricultura-de-precisao.

Simpson, P. K. (1989). Artificial neural systems: foundations, paradigms, applications, and implementations. New York: Pergamon Press.

Sims, D. A.; Gamon, J. A. (2002). Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment, 81, 337-354. DOI: https://doi.org/10.1016/S0034-4257(02)00010-X

Souza, A. V.; et al. (2021). Antioxidant activity, bioactive compounds, and agro‐industrial quality: correlations between parameters in fresh and processed tomatoes. Journal Of Food Processing and Preservation, [S.L.], 45 (9). Wiley. http://dx.doi.org/10.1111/jfpp.15696. DOI: https://doi.org/10.1111/jfpp.15696

Vasconcelos R. L. et al. (2020). Multivariate Behavior of Irrigated Sugarcane with Phosphate Fertilizer and Filter Cake Management: Nutritional State, Biometry, and Agroindustrial Performance. Journal of Soil Science and Plant Nutrition. https://doi.org/10.1007/s42729-020-00234-w. DOI: https://doi.org/10.1007/s42729-020-00234-w

Vieira, E. L. (2019). Apontamentos e práticas de fisiologia pós-colheita de frutos e hortaliças. Cruz das Almas (BA). https://proeducacional.com/download-apostila-cpa-20/.

Ferreira M. D; Embrapa et al. (2008). Colheita e Beneficiamento de Frutas e Hortaliças. São Carlos. https://ainfo.cnptia.embrapa.br/digital/bitstream/CNPDIA-2009-09/11483/1/LI_2008.pdf.

Widrow, B. & Lehr, M. A. (1990). 30 years of adaptive neural networks: perceptron, madaline, and backpropagation. Proceedings of the IEEE, 78 (9), 1415-1442. https://www-isl.stanford.edu/~widrow/papers/j199030years.pdf. DOI: https://doi.org/10.1109/5.58323

Yokomizo, G. K. Aspectos do melhoramento genético da bananeira. In: Dias, J. Do S. A.; Barreto, M. C. (Ed.). (2011). Aspectos agronômicos, fitopatológicos e socioeconômicos da sigatoka-negra na cultura da bananeira no Estado do Amapá. Embrapa Amapá, 3, 22-40.

Zuben, F. J. V. & Boccato, L. (2021). Redes Neurais com Função de Ativação de Base Radial. DCA/FEEC/Unicamp: Campinas. Notas de aula da disciplina Inteligência Artificial – Unicamp. https://www.dca.fee.unicamp.br/~lboccato/topico_4.3_redes_RBF.pdf.