Internet of Things (IoT) and Machine Learning (ML) Assisted Reference Evapotranspiration (ETO) Estimations

  • Rab Nawaz Bashir Department of Computer Science, COMSATS University Islamabad, Vehari Campus, Pakistan
  • Rana Muhammad Saleem Department of Computer Science, University of Agriculture, Faisalabad, Pakistan
  • Zahid Abbas Department of Computer Science, COMSATS University Islamabad, Vehari Campus, Pakistan
  • Haris Ali Khan Department of Computer Science, COMSATS University Islamabad, Vehari Campus, Pakistan
  • Dewan Muhammad Qaseem Hussain Department of Computer Science, COMSATS University Islamabad, Vehari Campus, Pakistan
  • Sarfaraz Natha Department of Software Engineering, Sir Syed University of Engineering & Technology, Karachi, Pakistan
Keywords: Internet of Things (IoT), Naïve Bays, Precision Irrigation (PI), Precision Agriculture (PA), Evapotranspiration (ET), Reference Evapotranspiration (ETo), Machine Learning (ML)

Abstract

Reference Evapotranspiration (ETo) is the amount of irrigation water required by a model crop to grow at its optimal level. ETo determination is a complex process that requires complicated calculations with many variables involved. There is a need to determine the ETo from available environmental conditions. Internet of Things (IoT) and Machine Learning (ML) based ETo estimation is proposed. IoT-assisted directly captured temperature data from the crop field is used to estimate the ETo. The estimated ETo can be used in many Precisions Agriculture (PA) applications especially in Precision Irrigation (PI) for measurement of Crop Potential Evapotranspiration (ETc), (irrigation water requirements of specific crops) using crop coefficient (Kc). Naive Bays ML algorithm is applied and evaluated for accurate estimations of ETo. The performance of the ML model is evaluated based on accuracy, f-measures, and recall for ETo estimation. Blaney-Criddle method of ETo measurements is used as a standard approach to benchmark the performance of the ML-based ETo estimations.

 

Published
2021-12-27
How to Cite
Bashir, R. N., Saleem, R. M., Abbas, Z., Khan, H. A., Hussain, D. M. Q., & Natha, S. (2021). Internet of Things (IoT) and Machine Learning (ML) Assisted Reference Evapotranspiration (ETO) Estimations. Quaid-E-Awam University Research Journal of Engineering, Science & Technology, Nawabshah., 19(2), 80-90. https://doi.org/10.52584/QRJ.1902.13