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A New Approach for Crop Rotation Problem in Farming 4.0

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Technological Innovation for Industry and Service Systems (DoCEIS 2019)

Abstract

Technology and innovations have long improved farming over the world and, as Industry 4.0 quickly spread, farmers have embraced high-level automation and data exchange, driving a transformation called Farming 4.0. Consequently, precise and even real-time field information have become easily accessible. Though, analyzing all this information requires great skills and tools, like mathematical knowledge and powerful computational algorithms to reach farmers expectations. This research explores the Crop Rotation Problem (CRP) and its relevance for the integration of Precision Agriculture (PA) and farm management. This paper presents a new mathematical approach for the CRP based on the nutrient balance and crop requirements, increasing the sustainable appealing of the problem. A real-encoded genetic algorithm (GA) was developed for optimization of the CRP. The results indicate good performance in mid and long-term crop scheduling.

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References

  1. Braun, A.-T., Colangelo, E., Steckel, T.: Farming in the era of industrie 4.0. In: 51st CIRP Conference on Manufacturing System, pp. 979–984 (2018)

    Article  Google Scholar 

  2. Pereira, A., Romero, F.: A review of the meanings and the implications of the Industry 4.0 concept. In: Manufacturing Engineering Society International Conference 2017, pp. 1206–1214 (2017)

    Article  Google Scholar 

  3. Pandey, G., Weber, R.J., Kumar, R.: Agricultural cyber-physical system: in-situ soil moisture and salinity estimation by dielectric mixing. IEEE Access 6, 43179–43191 (2018)

    Article  Google Scholar 

  4. Far, S.T., Rezaei-Moghaddam, K.: Impacts of the precision agricultural technologies in Iran: an analysis experts’ perception & their determinants. Inf. Process. Agric. 5, 173–184 (2018)

    Google Scholar 

  5. Bonneau, V., Copigneaux, B., Probst, L., Pedersen, B.: Industry 4.0 in agriculture: focus on IoT aspects. In: European Commission, Digital Transformation Monitor. https://ec.europa.eu/growth/tools-databases/dem/monitor/content/industry-40-agriculture-focus-iot-aspects

  6. United States Department of Agriculture (USDA): USDA Agricultural Projections to 2027. https://www.usda.gov/oce/commodity/projections/USDA_Agricultural_Projections_to_2027.pdf

  7. Empresa Brasileira de Pesquisa Agropecuária (Embrapa). https://www.embrapa.br/busca-de-noticias/-/noticia/13128392/brasil-lidera-investimentos-em-pesquisa-agricola-na-america-latina

  8. Memmah, M., Lescourret, F., Yao, X., Lavigne, C.: Metaheuristics for agricultural land use optimization. A review. Agron. Sustain. Dev. 365, 975–998 (2015)

    Article  Google Scholar 

  9. Santos, L.M.R., Michelon, P., Arenales, M.N., Santos, R.H.S.: Crop rotation scheduling with adjacency constraints. Ann. Oper. Res. 190, 165–180 (2008)

    Article  MathSciNet  Google Scholar 

  10. Aliano, A., Florentino, H., Pato, M.: Metaheuristics for a crop rotation problem. Int. J. Metaheuristics 3, 199–222 (2014)

    Article  Google Scholar 

  11. Aliano, A., Florentino, H, Pato, M.: Metodologias de escalarizações para o problema de rotação de culturas biobjetivo. In: Proceeding Series of the Brazilian Society of Applied and Computational Mathematics, vol. 6 (2018)

    Google Scholar 

  12. Watson, C., et al.: A review of farm-scale nutrient budgets for organic farms as a tool for management of soil fertility. Soil Use Manag. 18, 264–273 (2002)

    Article  Google Scholar 

  13. Berry, P., et al.: N, P and K budgets for crop rotations on nine organic farms in the UK. Soil Use Manag. 19, 112–118 (2003)

    Article  Google Scholar 

  14. United States Department of Agriculture (USDA): Vegetables Usual Planting and Harvesting Dates. USDA (2007). https://naldc.nal.usda.gov/download/CAT30992961/PDF

  15. United States Department of Agriculture (USDA): Vegetables 2017 Summary. USDA (2017). https://www.nass.usda.gov/Publications/Todays_Reports/reports/vegean17.pdf

  16. Oregon State University: Oregon Agricultural Enterprise Budgets. http://arec.oregonstate.edu/oaeb/

  17. Mohler, C.L., Johnson, S.E.: Crop Rotation on Organic Farms: a Planning Manual. Natural Resource, Agriculture, and Engineering Service, Ithaca (2009)

    Google Scholar 

  18. Knowles, J., Corne, D., Deb, K.: Multiobjective Problem Solving from Nature: From Concepts to Applications. Springer-Verlag, Berlin (2008). https://doi.org/10.1007/978-3-540-72964-8

    Book  MATH  Google Scholar 

  19. Bäck, T., Fogel, D.B., Michalewicz, Z.: Evolutionary Computation 1: Basic Algorithms and Operators. Institute of Physics Publishing, Bristol (2000)

    Book  Google Scholar 

  20. Bäck, T., Fogel, D.B., Michalewicz, Z.: Evolutionary Computation 1:2 Advanced Algorithms and Operators. Institute of Physics Publishing, Bristol (2000)

    Book  Google Scholar 

  21. Coello, C.A.C.: Evolutionary Multiobjective Optimization: Current and Future Challenges. Advances in Soft Computing. Springer, London (2003)

    Google Scholar 

  22. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, Berlin (1996)

    Book  Google Scholar 

  23. Deep, K., Singh, K.P., Kansal, M., Mohan, C.: A new crossover operator for real coded algorithms. Appl. Math. Comput. 188, 895–911 (2007)

    MathSciNet  Google Scholar 

  24. Deep, K., Singh, K.P., Kansal, M., Mohan, C.: A real coded genetic algorithm for solving integer and mixed integer optimization problems. Appl. Math. Comput. 212, 505–518 (2009)

    MathSciNet  MATH  Google Scholar 

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Correspondence to Bruno S. Miranda .

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Miranda, B.S., Yamakami, A., Rampazzo, P.C.B. (2019). A New Approach for Crop Rotation Problem in Farming 4.0. In: Camarinha-Matos, L., Almeida, R., Oliveira, J. (eds) Technological Innovation for Industry and Service Systems. DoCEIS 2019. IFIP Advances in Information and Communication Technology, vol 553. Springer, Cham. https://doi.org/10.1007/978-3-030-17771-3_9

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  • DOI: https://doi.org/10.1007/978-3-030-17771-3_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-17770-6

  • Online ISBN: 978-3-030-17771-3

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