Advertisement

An Online Learning Method for Embedded Decision Support in Agriculture Irrigation

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 687)

Abstract

In view of the advantages of Wireless Sensor Networks (WSNs), acquisition devices, environmental user-interfaces and low-cost monitoring systems in the agricultural and farming domain, an innovative use of automatic learning decision support is proposed to manage the irrigation process. The aim of this work is to develop a low computational cost technique for a smart irrigation support system, which can be implemented into a simple microcontroller. The classical methods in automatic irrigation use basic on-off controller or complex models with a large number of variables and focus to replace the farmer in the control scheme. Conversely, this proposal uses the farmer experience as the center of the closed loop interpreting its irrigation rules and adapting to the crop changes depending on growth cycle, weather and soil sensors’ signals without involving any model. The three weeks dataset for the testing was constructed from a one-week experimental setup with soil-water potential, soil temperature and sunlight sensors by using approximation functions. Moreover, an online algorithm (AdaDelta) based on gradient descent was tested in an adaptive binary classification with a single layer neuron via MATLAB simulation. Preliminary results of this application have shown its potential with an accuracy of 97% and 6.5% mean square error over the reference method, which poses new possibilities to work in this approach and generate precision agriculture applications for low cost and common irrigation plants by using the new age technologies.

Keywords

Precision agriculture Water management Automated irrigation Adaptive learning 

Notes

Acknowledgements

This research is being developed with support of the Universidad de Ibagué project no. 15-367-INT. The results presented in this paper have been obtained with the assistance of students from the Research Hotbed on Instrumentation and Control (SI2C), Research Group D+TEC, Universidad de Ibagué, Ibagué-Colombia.

References

  1. 1.
    Food and Agriculture Organisation: How to feed the world in 2050. In: Proceedings of a Technical Meeting of Experts, Rome Italy, 24–26 June 2009 (2009)Google Scholar
  2. 2.
    Lobell, D.B., Schlenker, W., Costa-Roberts, J.: Climate trends and global crop production since 1980. Science 333(6042), 616–620 (2011)CrossRefGoogle Scholar
  3. 3.
    Peng, S., et al.: Rice yields decline with higher night temperature from global warming. Proc. Natl. Acad. Sci. U. S. A. 101(27), 9971–9975 (2004)CrossRefGoogle Scholar
  4. 4.
    UNESCO and T. United: Water in a Changing World, vol. 11, no. 4 (2009)Google Scholar
  5. 5.
    IDEAM: Estudio nacional del agua, Minist. Medio Ambient, p. 253 (2014)Google Scholar
  6. 6.
    ONU: Sustainable Development Goals. United Nations (2017). http://www.un.org/sustainabledevelopment/sustainable-development-goals/
  7. 7.
    Cai, X.L., Sharma, B.R.: Integrating remote sensing, census and weather data for an assessment of rice yield, water consumption and water productivity in the Indo-Gangetic river basin. Agric. Water Manag. 97(2), 309–316 (2010)CrossRefGoogle Scholar
  8. 8.
    Xu, J., Liu, X., Yang, S., Qi, Z., Wang, Y.: Modeling rice evapotranspiration under water-saving irrigation by calibrating canopy resistance model parameters in the Penman-Monteith equation. Agric. Water Manag. 182, 55–66 (2017)CrossRefGoogle Scholar
  9. 9.
    Birendra, K.: Irrigation Scheduling: a Soft Adaptor to Weather Uncertainties and Irrigation Efficiency Improvement Initiatives (2016)Google Scholar
  10. 10.
    Aladenola, O., Madramootoo, C.: XVII th World Congress of the International Commission of Agricultural and Biosystems Engineering (CIGR) Development of a Model for Estimation Current and Future Irrigation Water Demand in Canada (2010)Google Scholar
  11. 11.
    Braneon, C.V.: Agricultural water demand assessment in the Southeast U.S. under climate change (2014)Google Scholar
  12. 12.
    Ogata, K.: Modern Control Engineering. Prentice Hall, Upper Saddle River (2010)zbMATHGoogle Scholar
  13. 13.
    Shalev-Shwartz, S.: Online learning and online convex optimization. Found. Trends® Mach. Learn. 4(2), 107–194 (2012)CrossRefzbMATHGoogle Scholar
  14. 14.
    Kelleher, J.D., Mac Namee, B., D’Arcy, A.: Fundamentals of machine learning for predictive data analytics: algorithms, worked examples, and case studies (2015)Google Scholar
  15. 15.
    Huang, Y., Li, C.: Real-time monitoring system for paddy environmental information based on DC powerline communication technology. Comput. Electron. Agric. 134, 51–62 (2017)CrossRefGoogle Scholar
  16. 16.
    Gutierrez, J., Villa-Medina, J.F., Nieto-Garibay, A., Porta-Gandara, M.A.: Automated irrigation system using a wireless sensor network and GPRS module. IEEE Trans. Instrum. Meas. 63(1), 166–176 (2014)CrossRefGoogle Scholar
  17. 17.
    Vellidis, G., Tucker, M., Perry, C., Kvien, C., Bednarz, C.: A real-time wireless smart sensor array for scheduling irrigation. Comput. Electron. Agric. 61(1), 44–50 (2008)CrossRefGoogle Scholar
  18. 18.
    Nikolidakis, S.A., Kandris, D., Vergados, D.D., Douligeris, C.: Energy efficient automated control of irrigation in agriculture by using wireless sensor networks. Comput. Electron. Agric. 113, 154–163 (2015)CrossRefGoogle Scholar
  19. 19.
    Intrigliolo, D.S., Castel, J.R.: Continuous measurement of plant and soil water status for irrigation scheduling in plum. Irrig. Sci. 23(2), 93–102 (2004)CrossRefGoogle Scholar
  20. 20.
    Nautiyal, M., Grabow, G.L., Huffman, R.L., Miller, G.L., Bowman, D.: Residential irrigation water use in the central piedmont of North Carolina. II: evaluation of smart irrigation technologies. J. Irrig. Drain. Eng. 141(4), 4014062 (2015)CrossRefGoogle Scholar
  21. 21.
    Romero, R., Muriel, J.L., García, I., Muñoz de la Peña, D.: Research on automatic irrigation control: state of the art and recent results. Agric. Water Manag. 114, 59–66 (2012)CrossRefGoogle Scholar
  22. 22.
    Navarro-Hellín, H., Martínez-del-Rincon, J., Domingo-Miguel, R., Soto-Valles, F., Torres-Sánchez, R.: A decision support system for managing irrigation in agriculture. Comput. Electron. Agric. 124, 121–131 (2016)CrossRefGoogle Scholar
  23. 23.
    Fisher, D.K., Kebede, H.: A low-cost microcontroller-based system to monitor crop temperature and water status. Comput. Electron. Agric. 74, 168–173 (2010)CrossRefGoogle Scholar
  24. 24.
    McCarthy, A.C., Hancock, N.H., Raine, S.R.: Advanced process control of irrigation: the current state and an analysis to aid future development. Irrig. Sci. 31(3), 183–192 (2013)CrossRefGoogle Scholar
  25. 25.
    Anaya-Isaza, A.J., Peluffo-Ordoñez, D.H., Ivan-Rios, J., Castro-Silva, J.A., Ruiz, D.A.C., Llanos, L.H.E.: Sistema de Riego Basado En La Internet De Las Cosas (IoT) Internet of Things for Irrigation System (IoT)Google Scholar
  26. 26.
    Shock, C.C., Wang, F.X., Flock, R., Feibert, E., Shock, C.A., Pereira, A.: Irrigation Monitoring Using Soil Water TensionGoogle Scholar
  27. 27.
    Rhoads, F.M., Yonts, C.D.: Irrigation Scheduling for Corn - Why and How, National Corn Handbook, vol. NCH-20, Electronic version (2000)Google Scholar
  28. 28.
    Maíz|Irritec – Sitemas de riego. http://www.irritec.com/es/soluciones/agricultura/maiz/. Accessed 08 Apr 2017
  29. 29.
    Villaú, J.M.: Manual Técnico del Manejo del Riego en el Cultivo de Maíz, SpainGoogle Scholar
  30. 30.
    Whalley, W.R., Ober, E.S., Jenkins, M.: Measurement of the matric potential of soil water in the rhizosphere. J. Exp. Bot. 64(13), 3951–3963 (2013)CrossRefGoogle Scholar
  31. 31.
    Toulis, P., Tran, D., Airoldi, E.: Stability and optimality in stochastic gradient descent. Harvard University, 12 May 2015Google Scholar
  32. 32.
    Irrometer: Basics. http://www.irrometer.com/basics.html. Accessed 15 May 2017
  33. 33.
    Ruder, S.: An overview of gradient descent optimization algorithms, September 2016Google Scholar
  34. 34.
    Wang, L., Yang, Y., Min, R., Chakradhar, S.: Accelerating deep neural network training with inconsistent stochastic gradient descent. Neural Netw. 93, 219–229 (2017)CrossRefGoogle Scholar
  35. 35.
    Zeiler, M.D.: ADADELTA: An Adaptive Learning Rate Method. Cornell University, December 2012Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Electronics, Faculty of EngineeringUniversidad de IbaguéIbaguéColombia

Personalised recommendations