Skip to main content
SpringerLink
Log in

An Application and Integration of Machine Learning Approach on a Real IoT Agricultural Scenario

  • Conference paper
  • First Online:
Pattern Recognition and Artificial Intelligence (ICPRAI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12068))

  • 1382 Accesses

Abstract

The Internet of Things (IoT) paradigm applied to the agriculture field provides a huge amount of data allowing the employment of Artificial Intelligence for multiple tasks. In this work, solar radiation prediction is considered. To the aim, Multi-Layer Perceptron is adopted considering a complete real complex use case and real-time working conditions. More specifically the forecasting system is integrated considering three different time forecasting horizons and, given different sites, needs and data availability, multiple input features configurations have been considered. The described work allows companies to innovate and optimize their industrial business.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Diagne, M., Dvid, M., Lauret, P., Boland, J., Schmutz, N.: Review of solar irradiance forecasting methods and a proposition for small-scale insular grids. Renew. Sustain. Energy Rev. 27, 65–76 (2013)

    Article  Google Scholar 

  2. Voyant, C., et al.: Machine learning methods for solar radiation forecasting: a review. Renew. Energy 105, 569–582 (2017)

    Article  Google Scholar 

  3. Inman, R.H., Pedro, H.T.C., Coimbra, C.F.M.: Solar forecasting methods for renewable energy integration. Prog. Energy Combust. Sci. 39(6), 535–576 (2013)

    Article  Google Scholar 

  4. Balducci, F., Impedovo, D., Pirlo, G.: Machine learning applications on agricultural datasets for smart farm enhancement. Machines 6, 38 (2018)

    Article  Google Scholar 

  5. Allen, R.G., Pereira, L.S., Raes, D., Smith, M.: Crop evapotranspiration - guidelines for computing crop water 2 requirements. FAO irrigation and drainage. Paper 56 Food and Agriculture ONU (1998). ISBN 978-92-5-104219-9

    Google Scholar 

  6. Dobbs, A., Florita, A.: Short-term solar forecasting performance of popular machine learning algorithms. In: International Workshop on the Integration of Solar Power into Power Systems (Solar Integration Workshop), pp. 1–14 (2017)

    Google Scholar 

  7. Meenal, R., Selvakumar, A.I.: Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters. Renew. Energy 121, 324–343 (2018). Elsevier

    Article  Google Scholar 

  8. Pierro, M., et al.: Multi-model ensemble for day ahead prediction of photovoltaic power generation. Solar Energy 134, 132–146 (2016). Elsevier

    Article  Google Scholar 

  9. Ding, M., Wang, L., Bi, R.: An ANN-based approach for forecasting the power output of photovoltaic system. Procedia Environ. Sci. 11, 1308–1315 (2011). Elsevier

    Article  Google Scholar 

  10. Chaouachi, A., Kamel, R., Nagasaka, K.: Neural network ensemble-based solar power generation short-term forecasting. Int. J. Electr. Comput. Eng. 3(6), 41–46 (2010). IAES

    Google Scholar 

  11. Chakraborty, K., Mehrotra, K., Mohan, C.K., Ranka, S.: Forecasting the behavior of multivariate time series using neural networks. Neural Netw. 5(6), 961–970 (1992)

    Article  Google Scholar 

  12. Bontempi, G., Ben Taieb, S., Le Borgne, Y.-A.: Machine learning strategies for time series forecasting. In: Aufaure, M.-A., Zimányi, E. (eds.) eBISS 2012. LNBIP, vol. 138, pp. 62–77. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-36318-4_3

    Chapter  Google Scholar 

  13. Ren, Y., Suganthan, P.N., Srikanth, N.: Ensemble methods for wind and solar power forecasting - a state-of-the-art review. Renew. Sustain. Energy Rev. 50, 82–91 (2015). https://doi.org/10.1016/j.rser.2015.04.081

    Article  Google Scholar 

  14. Ren, Y., Member, S., Zhang, L., Member, S., Suganthan, P.N.: Ensemble classification and regression-recent developments, applications and future directions. IEEE Comput. Intell. Mag. 11, 1–14 (2016)

    Article  Google Scholar 

  15. Rekha, P., Rangan, V.P., Ramesh, M.V., Nibi, K.V.: High yield groundnut agronomy: An IoT based precision farming framework. In: Proceedings of the 2017 IEEE Global Humanitarian Technology Conference, San Jose, CA, USA, 19–22 October 2017, pp. 1–5 (2017)

    Google Scholar 

  16. Sarangdhar, A.A., Pawar, V.R.: Machine learning regression technique for cotton leaf disease detection and controlling using IoT. In: Proceedings of the 2017 International Conference of Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, 20–22 April 2017, vol. 2, pp. 449–454 (2017)

    Google Scholar 

  17. Patil, S.S., Thorat, S.A.: Early detection of grapes diseases using machine learning and IoT. In: Proceedings of the 2016 2nd International Conference on Cognitive Computing and Information Processing (CCIP), Mysore, India, 12–13 August 2016, pp. 1–5 (2016)

    Google Scholar 

  18. Wolfert, S., Ge, L., Verdouw, C., Bogaardt, M.J.: Big data in smart farming a review. Agric. Syst. 153, 69–80 (2017)

    Article  Google Scholar 

  19. Biradarand, H.B., Shabadi, L.: Review on IoT based multidisciplinary models for smart farming. In: Proceedings of the 2nd IEEE Int. Conference on Recent Trends in Electronics, Information Communication Technology, Bangalore, India, 19–20 May 2017, pp. 1923–1926 (2017)

    Google Scholar 

  20. Yoon, C., Huh, M., Kang, S.G., Park, J., Lee, C.: Implement smart farm with IoT technology. In: Proceedings of the 20th International Conference on Advanced Communication Technology, Chuncheon-si Gangwon-do, Korea, 11–14 February 2018, pp. 749–752 (2018)

    Google Scholar 

  21. Inman, R.H., Pedro, H.T.C., Coimbra, C.F.M.: Solar forecasting methods for renewable energy integration. Progr. Energy Combust. Sci. 39(6), 535–576 (2013)

    Article  Google Scholar 

  22. Rotondi, D., Straniero, L., Saltarella, M., Balducci, F., Impedovo, D., Pirlo, G.: Semantics for wastewater reuse in agriculture*. In: 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC), Bari, Italy 2019, pp. 598–603 (2019)

    Google Scholar 

Download references

Acknowledge

This work is within the Eco-Loop project (no. 2AT8246) funded by the Puglia POR FESR-FSE 2014-2020. Fondo Europeo Sviluppo Regionale. Azione 1.6 - Avviso Pubblico “InnoNetwork”.

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Bari Aldo Moro, Bari, 70125, Italy

    Donato Impedovo, Fabrizio Balducci, Lucia Sarcinella, Mariagiorgia AgneseTandoi & Giuseppe Pirlo

  2. Sysman Progetti and Servizi S.R.L., Bari, 70125, Italy

    Giulio D’Amato, Michela Del Prete & Erminio Riezzo

Authors
  1. Donato Impedovo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fabrizio Balducci
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Giulio D’Amato
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michela Del Prete
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Erminio Riezzo
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lucia Sarcinella
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mariagiorgia AgneseTandoi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Giuseppe Pirlo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Donato Impedovo .

Editor information

Editors and Affiliations

  1. East China Normal University, Shanghai, China

    Yue Lu

  2. Paris Descartes University, Paris, France

    Nicole Vincent

  3. Hong Kong Baptist University, Kowloon, Hong Kong

    Pong Chi Yuen

  4. Sun Yat-sen University, Guangzhou, China

    Wei-Shi Zheng

  5. Polytechnique Montréal, Montreal, QC, Canada

    Farida Cheriet

  6. Concordia University, Montreal, QC, Canada

    Ching Y. Suen

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Impedovo, D. et al. (2020). An Application and Integration of Machine Learning Approach on a Real IoT Agricultural Scenario. In: Lu, Y., Vincent, N., Yuen, P.C., Zheng, WS., Cheriet, F., Suen, C.Y. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2020. Lecture Notes in Computer Science(), vol 12068. Springer, Cham. https://doi.org/10.1007/978-3-030-59830-3_41

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-59830-3_41

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59829-7

  • Online ISBN: 978-3-030-59830-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation