| description abstract | The present study addresses the applicability of the crop water stress index (CWSI) derived from canopy temperature to detect the crop water stress of paddy crop. The performance of two artificial intelligence techniques, adaptive neuro-fuzzy inference system (ANFIS) and self-organizing map (SOM), are compared while determining the CWSI of paddy crop. Field experiments were conducted with varying irrigation water applications during two seasons in 2021 and 2022. The ANFIS and SOM-simulated CWSI values were compared with the experimentally calculated CWSI (EP-CWSI). Multiple regression analysis was used to determine the upper and lower CWSI baselines. The upper CWSI baseline was found to be a function of crop height and wind speed, while the lower CWSI baseline was a function of crop height, air vapor pressure deficit, and wind speed. The performance of ANFIS and SOM were compared based on mean absolute error (MAE), mean bias error (MBE), root mean squared error (RMSE), index of agreement (d), Nash–Sutcliffe efficiency (NSE), and coefficient of correlation (R2). The ANFIS (R2=0.81, NSE=0.73, d=0.94, RMSE=0.04, MAE=0.00–1.76 and MBE=−2.13–1.32) outperformed the SOM model (R2=0.77, NSE=0.68, d=0.90, RMSE=0.05, MAE=0.00–2.13 and MBE=−2.29–1.45). Overall, the results suggest that ANFIS is a reliable tool for accurately determining CWSI in paddy crops compared to SOM. | |
