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An Intelligent Weather Station Design for Machine Learning in Precisions Irrigation Scheduling

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Emergent Converging Technologies and Biomedical Systems

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 841))

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Abstract

Agriculture 4.0 has taken a big trend in society focusing on precision irrigation based on Internet of Things and machine learning techniques. Agriculture 4.0 is intended as the demand increases, due to the rise of population and climate changes. So, focusing on precision water management, an innovative model is required to provide better functionality. A weather station is set to collect precise data from the atmosphere. The collected data, which is of paramount importance in crop water demand and prediction, is transmitted over an Internet of Things (IoT) cloud server. The received data from the weather station for local weather conditions is in ready to use format by various machine learning algorithms to predict irrigation requirements. The system design provides a cost-effective solution to Agriculture 4.0 in comparison with commercial weather stations.

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Abbreviations

IoT:

Internet of Things

CWSI:

Crop Water Stress Index

ELM:

Extreme Learning Machine

ANN:

Artificial Neural Network

MARS:

Multivariate Adaptive Regression Splines

GRNN:

Generalized Regression Neural Network

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Acknowledgements

This paper and the research behind it would not have been possible without the exceptional support of my supervisor. This research was partially supported by Lovely Professional University, Phagwara, Punjab, India.

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Authors and Affiliations

  1. Lovely Professional University, Phagwara, Punjab, India

    Dushyant Kumar Singh & Rajeev Sobti

Authors
  1. Dushyant Kumar Singh
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  2. Rajeev Sobti
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Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, University Institute of Engineering and Technology, Kurukshetra, Haryana, India

    N. Marriwala

  2. Dean Engineering and Technology, Kurukshetra University, Kurukshetra, Haryana, India

    C. C. Tripathi

  3. Department of Electronic and Communication Engineering, Jaypee University of Information Technology, Solan, Himachal Pradesh, India

    Shruti Jain

  4. Co-Founder of Dew Mobility, Santa Clara University, Santa Clara, CA, USA

    Shivakumar Mathapathi

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Singh, D., Sobti, R. (2022). An Intelligent Weather Station Design for Machine Learning in Precisions Irrigation Scheduling. In: Marriwala, N., Tripathi, C.C., Jain, S., Mathapathi, S. (eds) Emergent Converging Technologies and Biomedical Systems . Lecture Notes in Electrical Engineering, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-16-8774-7_5

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  • DOI: https://doi.org/10.1007/978-981-16-8774-7_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-8773-0

  • Online ISBN: 978-981-16-8774-7

  • eBook Packages: EngineeringEngineering (R0)

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