Neural computing modelling of the crop water stress index

Navsal Kumar, Adebayo J. Adeloye, Vijay Shankar, Rabee Rustum

Research output: Contribution to journalArticlepeer-review

34 Citations (Scopus)


In this study, two artificial neural network models viz. supervised Feed-Forward Back Propagation (FF-BP) and unsupervised Kohonen Self-Organizing Map (K-SOM) have been developed to predict the Crop Water Stress Index (CWSI) using air temperature, relative humidity, and canopy temperature. Field experiments were conducted on Indian mustard to observe the crop canopy temperature under different levels of irrigation treatment during the 2017 and 2018 cropping seasons. The empirical CWSI was computed using well-watered and non-transpiring baseline canopy temperatures. The K-SOM and FF-BP CWSI predictions were compared with the empirical CWSI estimates and both performed satisfactorily. Of the two, however, the K-SOM was better with R2 (coefficient of determination) of 0.97 and 0.96 for model development and validation, respectively; corresponding values for FF-BP were 0.86 and 0.75. The results of the study suggest that neural network modelling offers significant potential for reliable prediction of the CWSI, which can be utilized in irrigation scheduling and crop stress management.
Original languageEnglish
Article number106259
JournalAgricultultural Water Management
Early online date17 May 2020
Publication statusPublished - 1 Sept 2020


  • artificial neural networks
  • Self-Organizing Map
  • Taylor diagram
  • Crop water stress index
  • Indian mustard

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Artificial Intelligence
  • Environmental Engineering


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