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Comparative Study of Regression Models Towards Performance Estimation in Soil Moisture Prediction

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Advances in Computing and Data Sciences (ICACDS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 906))

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Abstract

The global food demand is increasing with the increase in world population. The agriculture land and fresh water resources are limited and the water crisis is further enhanced due to the global warming and the shortfall of better water management systems. The precision agriculture can play a very important role in curtailing this crisis by improving irrigation management techniques. These techniques can efficiently utilize the natural rainwater and ground water. It is also beneficial for the energy saving and achieving the better growth of crop. Further, to maintain the proper growth with optimal pesticide, the soil moisture of crop is needed to be maintained during its whole life cycle. Moisture of soil is an essential aspect for hydrology system that represents the typical circumstances in a limited volume of soil. An effective prediction of soil moisture can save the water and energy and it is essential to develop effective irrigation management system for this purpose. Prediction of soil moisture is vital for better irrigation management system. This paper describes result of experimental scenario for different machine learning regression techniques to predict the soil moisture.

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Acknowledgements

Authors sincerely express thanks to The Director, CSIR-CSIO, and Chandigarh, India for support to this research work at CSIR-CSIO. Furthermore, authors acknowledge Sh. Suman Tewary for providing valuable suggestions.

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Authors and Affiliations

  1. National Institute of Technical Teachers Training and Research (NITTTR), Chandigarh, 160019, India

    Amarendra Goap & C. Rama Krishna

  2. CSIR-Central Scientific Instruments Organization, Chandigarh, 160030, India

    Amarendra Goap, Deepak Sharma & A. K. Shukla

Authors
  1. Amarendra Goap
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  2. Deepak Sharma
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  3. A. K. Shukla
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  4. C. Rama Krishna
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Corresponding author

Correspondence to Amarendra Goap .

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Editors and Affiliations

  1. University of KwaZulu-Natal, Durban, South Africa

    Mayank Singh

  2. Jaypee University of Information Technology, Solan, India

    P. K. Gupta

  3. Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

    Vipin Tyagi

  4. Institute of Information Theory and Automation, Prague 8, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, Canada

    Tuncer Ören

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Goap, A., Sharma, D., Shukla, A.K., Krishna, C.R. (2018). Comparative Study of Regression Models Towards Performance Estimation in Soil Moisture Prediction. In: Singh, M., Gupta, P., Tyagi, V., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2018. Communications in Computer and Information Science, vol 906. Springer, Singapore. https://doi.org/10.1007/978-981-13-1813-9_31

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  • DOI: https://doi.org/10.1007/978-981-13-1813-9_31

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  • Print ISBN: 978-981-13-1812-2

  • Online ISBN: 978-981-13-1813-9

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