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Design of an Intelligent Irrigation System Based on Fuzzy Logic

  • Fabián Cuzme-RodríguezEmail author
  • Edgar Maya-Olalla
  • Leandro Salazar-CárdenasEmail author
  • Mauricio Domínguez-Limaico
  • Marcelo Zambrano Vizuete
Conference paper
  • 56 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1194)

Abstract

The present study contributes to the improvement of the processes of conventional agriculture that are still being carried out independently of the Information and Communication Technologies, which show shortcomings in the forms of irrigation carried out suffering an impact on the use of the water supply. From this point, precision agriculture becomes indispensable to improve the processes of agricultural production processes, allowing adequate management of agricultural plots supported by the use of technology to estimate, evaluate and understand the variations of the variables involved and offer quantities of water necessary for cultivation. This analysis covers the design and construction of an intelligent irrigation system based on fuzzy logic applied in vegetable crops. The fundamental mechanism of this system is to realize the control of irrigation through a scheme consisting of two modules, the data acquisition module, and the decision-making module. Considering that the section with the highest degree of responsibility is the integration of fuzzy logic as a control mechanism and that is part of the decision-making module. To achieve this, meteorological variables such as precipitation, temperature, the humidity of the environment and soil moisture are evaluated, which are considered as input variables for the diffuse system. The operation of the prototype is crystallized in a functional graphical interface and tested in two scenarios, where its efficiency in the proper use of the water supply is demonstrated.

Keywords

Fuzzy logic Intelligent system Irrigation system 

References

  1. 1.
    Salcedo, S., Guzman, L.: Agricultura familiar en America Latina y el Caribe: Recomendaciones de Política (2014)Google Scholar
  2. 2.
    Instituto Nacional de Estadística y Censos: Encuesta de superficie y producción agropecuaria continua ESPAC 2017 (2017)Google Scholar
  3. 3.
    Organización de las Naciones Unidas para la Agricultura y la Alimentación FAO: Enfoques: Mejorar la tecnología de riego (2003). http://www.fao.org/ag/esp/revista/0303sp3.htm. Accessed 21 Nov 2019
  4. 4.
    Demin, P.: Aportes para el mejoramiento del manejo de los sistemas de riego. Inst Nac Tecnol Agropecu, vol. 1, pp. 1–24 (2014)Google Scholar
  5. 5.
    Mohanraj, I., Gokul, V., Ezhilarasie, R., Umamakeswari, A.: Intelligent drip irrigation and fertigation using wireless sensor networks. In: Proceedings - 2017 IEEE Technological Innovations in ICT for Agriculture and Rural Development, TIAR 2017, pp. 36–41, Institute of Electrical and Electronics Engineers Inc. (2018)Google Scholar
  6. 6.
    Ogasawara, J.: Estudio de los diferentes sistemas de riego agrícola utilizados en el Paraguay (2017)Google Scholar
  7. 7.
    Alomar, B., Alazzam, A.: A Smart irrigation system using IoT and fuzzy logic controller. In: ITT 2018 - Information Technology Trends: Emerging Technologies for Artificial Intelligence, pp. 175–179, Institute of Electrical and Electronics Engineers Inc. (2019)Google Scholar
  8. 8.
    Pernapati, K.: IoT based low cost smart irrigation system. In: Proceedings of the International Conference on Inventive Communication and Computational Technologies, ICICCT 2018, pp. 1312–1315, Institute of Electrical and Electronics Engineers Inc. (2018)Google Scholar
  9. 9.
    Consejo Nacional de Planificación: Plan Nacional de Desarrollo 2017–2021 - Toda una Vida (2017)Google Scholar
  10. 10.
    Ministerio de Agricultura Gandaría Acuacultura y Pesca (MAGAP): La Política Agropecuaria Ecuatoriana: Hacia el desarrollo territorial rural sostenible 2015–2025 (2016)Google Scholar
  11. 11.
    Organización de las Naciones Unidas para la Educación la Ciencia y la Cultura: Informe Mundial de Naciones Unidas sobre el Desarrollo de los Recursos Hídricos (2019)Google Scholar
  12. 12.
    Savić, T., Radonjic, M.: WSN architecture for smart irrigation system. In: 2018 23rd International Scientific-Professional Conference on Information Technology, IT 2018, pp. 1–4, Institute of Electrical and Electronics Engineers Inc. (2018)Google Scholar
  13. 13.
    Valero, J., Picornell, R.: El Riego y sus Tecnologías (2010)Google Scholar
  14. 14.
    Wangoo, D.P.: Artificial intelligence techniques in software engineering for automated software reuse and design. In: 2018 4th International Conference on Computing Communication and Automation, ICCCA 2018, Institute of Electrical and Electronics Engineers Inc. (2018)Google Scholar
  15. 15.
    Chen, G., Yue, L.: Research of irrigation control system based on fuzzy neural network. In: Proceedings 2011 International Conference on Mechatronic Science, Electric Engineering and Computer, MEC 2011, pp. 209–212 (2011)Google Scholar
  16. 16.
    Ross, T.J., Ross, T.J.: Fuzzy Logic With Engineering Applicationes, 3rd edn. Wiley, Hoboken (2010)CrossRefGoogle Scholar
  17. 17.
    The scikit-image team: The scikit-fuzzy Documentation (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Fabián Cuzme-Rodríguez
    • 1
    Email author
  • Edgar Maya-Olalla
    • 1
  • Leandro Salazar-Cárdenas
    • 1
    Email author
  • Mauricio Domínguez-Limaico
    • 1
  • Marcelo Zambrano Vizuete
    • 1
  1. 1.Carrera de Ingeniería en TelecomunicacionesUniversidad Técnica del NorteIbarraEcuador

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