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Software Optimisation for Mechanised Sugarcane Planting Scenarios to Aid in Decision-Making

Sugar Tech volume 23, pages 86–93 (2021)Cite this article

Abstract

With advancements in the mechanisation of sugarcane farming, studies have been fundamental to improving the process—from soil preparation to harvest. Faced with increasing challenges of economic scenarios, alternatives should be sought aimed at optimising resources, reducing costs, improving operational efficiency, logistics, among others. Planting is one of the main agricultural operations, any deviation in this phase harms the crop during the crop cycle, so planning in advance the area to be planted is essential for better results. Analysis of better planting scenarios prior to harvest combined with the use of autopilot requires knowledge of the systematisation areas and skilled labour to guarantee the quality of the process and reduce losses and damages. The objective of this study is to both evaluate and optimise sugarcane planting scenarios based on travel and manoeuvre time, travel distance, number of manoeuvres, and fuel consumption. The study was conducted in the municipality of Tanabi, SP, during the 2013 planting season. The results showed fewer manoeuvres and longer planting lines in the optimised area, increased the availability of the machine and generated possible cost reduction.

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References

  • Benedini, Mauro S., and Armene J. Conde. 2008. Sistematização da Área para colheita mecanizada da cana-de-açúcar. Revista Canavieiros 29: 28–31.

    Google Scholar 

  • Bernardi, Alberto C.C., and Ricardo I. Yamassushi. 2014. Adoção da agricultura de precisão no Brasil. In Agricultura de precisão: resultados de um novo olhar, ed. Alberto C.C. Bernardi, João M. Naime, Álvaro V. Resende, Luís H. Bassoi, Ricardo Y. Inamasu, 559–577. Brasília: Embrapa.

  • Campos, Cassiano M., Marcos Milan, and Luiz F.F. Siqueira. 2008. Identificação e avaliação de variáveis críticas no processo de produção da cana-de-açúcar. Engenharia Rural 28(3): 554–564. https://doi.org/10.1590/S0100-69162008000300016.

    Article  Google Scholar 

  • CONAB—Companhia Nacional de Abastecimento. 2018. Acompanhamento da Safra Brasileira: cana-de-açúcar—Safra 2018/19. First survey. https://www.conab.gov.br/info-agro/safras/cana/boletim-da-safra-de-cana-de-acucar/item/download/17727_4e54c5103a0ab4a15529e35307c79b2e. Accessed January 17, 2019.

  • Fountas, Spyros, Søren M. Pedersen, and Simon Blackmore. 2005. ICT in precision agriculture—diffusion of technology. An overview of Precison Agriculture. https://doi.org/10.13140/2.1.1586.5606.

  • Gebbers, Robin, and Viacheslav I. Adamchuk. 2010. Precision agriculture and food security. Science 327(5967): 828–831. https://doi.org/10.1126/science.1183899.

    Article  CAS  PubMed  Google Scholar 

  • Hair Jr., Joseph F., Rolph E. Anderson, Ronald L. Tatham, and William C. Black. 2005. Análise multivariada de dados. Porto Alegre: Bookman.

    Google Scholar 

  • Hosseini, M., M. Chizari, and M. Bordbar. 2010. Evaluation of the possibility of precision agriculture from the view point of Agricultural experts in Fars Province. Iranian Agricultural Extension and Education Journal 6: 5–47.

    Google Scholar 

  • Kaiser, Henry F. 1958. The varimax criterion for analytic rotation in factor analysis. Psychometrika 23 (3): 187–200. https://doi.org/10.1007/BF02289233.

    Article  Google Scholar 

  • Maruyama, Teruo M., Sergio Silva Ribeiro, and Alaine M. Guimarães. 2014. Estudo Comparativo de Opções de Software de Processamento Digital de Imagens para Análise de Sementes. In Congresso Sul Brasileiro de Computação, ed. Ana Claudia G. Barbosa, Luciano Antunes, Merisandra C.M. Garcia, Paulo J. Martins, and Rogerio A. Casagrande. Porto Alegre: authors, edition. http://periodicos.unesc.net/sulcomp/article/view/1794/1696. Accessed January 19, 2019.

  • Mondal, Pinaki, and Manisha Basu. 2009. Adoption of precision agriculture technologies in India and in some developing countries: Scope, present status and strategies. Progress in Natural Science 19(6): 659–666. https://doi.org/10.1016/j.pnsc.2008.07.020.

    Article  Google Scholar 

  • Nunes Jr., Dib. 2008. As variedades e o planejamento do plantio. STAB: Açúcar, Álcool e Subproduto. Piracicaba: STAB.

  • Oliveira, Tiago C.A., and João Paulo Molin. 2011. Uso de piloto automático na implantação de pomares de citros. Engenharia Agrícola 31(2): 334–342. https://doi.org/10.1590/S0100-69162011000200013.

    Article  Google Scholar 

  • Ramos, Carlos R.G., Kleber P. Lanças, Ronilson S. Santos, Murilo B. Martins, and Jefferson Sandi. 2016. Eficiência e demanda energética de uma colhedora de cana-de-açúcar em talhões de diferentes comprimentos. Energia na Agricultura 31 (2): 121–128. https://doi.org/10.17224/EnergAgric.2016v31n2p121-128 .

    Article  Google Scholar 

  • Ramos, Rômulo P. 2014. Planejamento do plantio e da colheita de cana-de-açúcar utilizando técnicas matemáticas de otimização. Ph.D. Thesis. Faculdade de Ciências Agronômicas de Botucatu, Universidade Estadual Paulista, Botucatu, São Paulo, Brazil.

  • Santoro, Ever, Edilaine M. Soler, and Adriana Cherri. 2017. Route optimization in mechanized sugarcane harvesting. Computers and Eletronics in Agriculture 141: 140–146. https://doi.org/10.1016/j.compag.2017.07.013.

    Article  Google Scholar 

  • Santos, Neisvaldo B., Diego S. Cavalcante, Haroldo C. Fernandes, and Casimiro D. Galdanha Jr. 2014. Simulação da eficiência de campo da colheita mecanizada de cana-de-açúcar (Saccharum spp.). Energia na. Agricultura 29 (1): 09–13. https://doi.org/10.17224/EnergAgric.2014v29n1p09-13

    Article  Google Scholar 

  • Santos, Neisvaldo B., Haroldo C. Fernandes, and Casimiri D. Galdanha Jr. 2015. Economic impact of sugarcane (Saccharum spp.) loss in mechanical harvesting. Científica 43 (1): 16–21. https://doi.org/10.15361/1984-5529.2015v43n1p16-21.

    Article  Google Scholar 

  • Spekken, Mark, Sytze de Bruin, João Paulo Molin, and Gerd Sparovek. 2016. Planning machine paths and row crop patterns on steep surfaces to minimize soil erosion. Computers and Electronics in Agriculture 124: 194–210. https://doi.org/10.1016/j.compag.2016.03.013.

    Article  Google Scholar 

  • Trevisan, Rodrigo G., Mateus T. Eitelwein, Natanael S. Vilanova Jr, José V. Salvi, Bruna P. Passalaqua, José P. Molin. 2014. Avaliação da precisão dos sinais RTK e RTX em ensaio estático e cinemático. In Congresso Brasileiro de Agricultura de Precisão, ed. Carlos E. A. Furlani, David L. Rosalen, and José P. Molin. São Pedro: SBEA. https://www.agriculturadeprecisao.org.br/wp-content/uploads/2020/01/cgr-2014_03.pdf. Accessed January 19, 2019.

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Authors and Affiliations

  1. Department of Engineering and Exact Sciences, Laboratory of Agricultural Machinery and Mechanization, São Paulo State University, Jaboticabal, São Paulo, Brazil

    L. A. S. Nardo, A. R. Gonzaga, L. P. Oliveira & R. P. Silva

  2. University of Sorocaba, Sorocaba, São Paulo, Brazil

    C. S. S. Paixão

  3. Federal University São Carlos, Buri, São Paulo, Brazil

    M. A. Voltarelli

Authors
  1. L. A. S. Nardo
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  2. C. S. S. Paixão
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  3. A. R. Gonzaga
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  4. L. P. Oliveira
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  5. M. A. Voltarelli
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  6. R. P. Silva
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Correspondence to A. R. Gonzaga.

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Nardo, L.A.S., Paixão, C.S.S., Gonzaga, A.R. et al. Software Optimisation for Mechanised Sugarcane Planting Scenarios to Aid in Decision-Making. Sugar Tech 23, 86–93 (2021). https://doi.org/10.1007/s12355-020-00868-1

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  • DOI: https://doi.org/10.1007/s12355-020-00868-1

Keywords

  • Precision agriculture
  • AgroCAD®
  • Agricultural planning
  • Running time