Advertisement

Forecasting Soil Moisture Based on Evaluation of Time Series Analysis

  • Sukhwinder SinghEmail author
  • Sanmeet Kaur
  • Parteek Kumar
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 609)

Abstract

Precision agriculture is a technique that is incorporated to produce high crop yield with the best utilization of available resources. Traditional farming is adversely affected due to improper resource management. In order to overcome the efforts of a farmer, a model for the soil moisture forecasting has been proposed in this manuscript to deliver better after-effects of farming. The proposed model uses long short-term memory (LSTM) to predict soil moisture. The model is trained on a dataset acquired from IIT Kanpur agricultural site. For analyzing the performance of the model mean absolute error (MAE), mean absolute percentage error (MAPE), mean squared error (MSE), and root mean squared error (RMSE) has been used as performance metrics. This paper is paving way for the early prediction of the soil moisture that can be used with other advanced innovative irrigation techniques.

Keywords

Smart irrigation Time series Soil moisture LSTM MAE MSE 

Notes

Acknowledgements

The author would like to thanks Dr. Rajib Maity of IIT Kanpur, India for providing dataset based on the different depths of the soil moisture and also acknowledge to Council of Scientific and Industrial Research (CSIR) for funding grants vide No. 38(1464)/18/EMIR-II for carrying out research work.

References

  1. 1.
    Shinde D, Siddiqui N (2018) IOT based environment change monitoring and controlling in greenhouse using WSN. In: 2018 international conference on information, communication, engineering and technology (ICICET), IEEE, Aug 2018, pp 1–5Google Scholar
  2. 2.
    Calla OPN et al (2008) Measurement of soil moisture using microwave radiometer. In: Proceedings of the IEEE international conference on microwave ′08, 2008, pp 621–624Google Scholar
  3. 3.
    Zhang P, Zhang Q, Liu F, Li J, Cao N, Song C (2017) The construction of the integration of water and fertilizer smart water saving irrigation system based on big data. In: 2017 IEEE international conference on computational science and engineering (CSE) and IEEE international conference on embedded and ubiquitous computing (EUC), vol 2, pp 392–397. IEEE, July 2018Google Scholar
  4. 4.
    Lenka SK, Mohapatra AG (2015 December). Gradient descent with momentum based neural network pattern classification for the prediction of soil moisture content in precision agriculture. In: 2015 IEEE International Symposium on Nanoelectronic and Information Systems (pp. 63–66). IEEEGoogle Scholar
  5. 5.
    Ip RH, Ang LM, Seng KP, Broster JC, Pratley JE (2018) Big data and machine learning for crop protection. Comput Electron Agric 151:376–383CrossRefGoogle Scholar
  6. 6.
    Han J, Mao K, Xu T, Guo J, Zuo Z, Gao C (2018) A soil moisture estimation framework based on the CART algorithm and its application in China. J Hydrol 563:65–75CrossRefGoogle Scholar
  7. 7.
    Zhu Y, Song J, Dong F (2011) Applications of wireless sensor network in the agriculture environment monitoring. Procedia Eng 16:608–614CrossRefGoogle Scholar
  8. 8.
    Priya R, Ramesh D, Khosla E (2018) Crop prediction on the region belts of India: a Naïve Bayes MapReduce precision agricultural model. In: 2018 international conference on advances in computing, communications and informatics (ICACCI), pp 99–104. IEEE, Sept 2018Google Scholar
  9. 9.
    Ponce-Guevara KL, Palacios-Echeverría JA, Maya-Olalla E, Domínguez-Limaico HM, Suárez-Zambrano LE, Rosero-Montalvo PD, Peluffo-Ordóñez DH, Alvarado-Pérez JC (2017) GreenFarm-DM: a tool for analyzing vegetable crops data from a greenhouse using data mining techniques (First trial). In: 2017 IEEE second ecuador technical chapters meeting (ETCM), pp 1–6. IEEE, Oct 2017Google Scholar
  10. 10.
    Soni K, Kapoor S, Parmar KS, Kaskaoutis DG (2014) Statistical analysis of aerosols over the Gangetic-Himalayan region using ARIMA model based on long-term MODIS observations. Atmos Res 149:174–192CrossRefGoogle Scholar
  11. 11.
    Zhang L, Lin J, Qiu R, Hu X, Zhang H, Chen Q, Tan H, Lin D, Wang J (2018) Trend analysis and forecast of PM2.5 in Fuzhou, China using the ARIMA model. Ecol Ind 95:702–710CrossRefGoogle Scholar
  12. 12.
    Qing X, Niu Y (2018) Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM. Energy 148:461–468CrossRefGoogle Scholar
  13. 13.
    Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780CrossRefGoogle Scholar
  14. 14.
    Pal M, Maity R, Dey S (2016) Statistical modelling of vertical soil moisture profile: coupling of memory and forcing. Water Resour Manage 30(6):1973–1986CrossRefGoogle Scholar
  15. 15.
    Li Y, Cao H (2018) Prediction for tourism flow based on LSTM neural network. Procedia Comput Sci 129:277–283CrossRefGoogle Scholar
  16. 16.
  17. 17.
    Bouktif S, Fiaz A, Ouni A, Serhani M (2018) Optimal deep learning LSTM model for electric load forecasting using feature selection and genetic algorithm: Comparison with machine learning approaches. Energies 11(7):1636CrossRefGoogle Scholar
  18. 18.
    Sharma A, Rani R (2017) An optimized framework for cancer classification using deep learning and genetic algorithm. J Med Imag Health Inform 7(8):1851–1856CrossRefGoogle Scholar
  19. 19.
    Fischer T, Krauss C (2018) Deep learning with long short-term memory networks for financial market predictions. Eur J Oper Res 270(2):654–669MathSciNetCrossRefGoogle Scholar
  20. 20.
  21. 21.
  22. 22.
    Haiges R, Wang YD, Ghoshray A, Roskilly AP (2017) Forecasting electricity generation capacity in Malaysia: an auto regressive integrated moving average approach. Energy Procedia 105:3471–3478CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.CSEDThapar Institute of Engineering and TechnologyPatialaIndia

Personalised recommendations