Hybrid deep learning techniques for estimation of daily crop evapotranspiration using limited climate data

Smart agriculture aims to improve the quality and quantity of crops by efficiently managing available resources. One of the main components of smart agriculture is precision irrigation which applies the required amount of water at the right time to crops. Crop evapotranspiration (ETc) prediction can contribute to managing the irrigation strategies effectively. Although several methods have been introduced for estimating ETc values, these methods are still associated with various challenges and limitations (high cost, time-consuming, and meteorological data unavailability). The current study is motivated by the desire to create deep learning (DL) based models capable of estimating ETc reliably to eliminate the above-mentioned limitations and forecast future ETc values for adaptation strategies. In this paper, two-hybrid DL models, i.e., Convolution Neural Network-eXtreme Gradient Boosting (CNN-XGB) and Convolution Neural Network-Support Vector Regression (CNN-SVR) are proposed to estimate daily ETc values of wheat and rice crops. Further, limited climate data (minimum temperature (T-min), maximum temperature (T-max), mean temperature (T-max) and solar radiation (R-s)) is used for the prediction of ETc values to handle data-scarce situations. Also, the future climate data obtained using two emission scenarios: Representative Concentration Pathways (RCP) 4.5 and RCP 8.5 for the time period 2023-2033, are used to project changes in ETc. The results demonstrate that the proposed hybrid models provide satisfactory performance with the Nash-SutcliffeEfficiency (NSE) = 0.95 and 0.976 for rice and wheat ETc values, respectively. The simulation of future data reveals the increase in T-min by 7.03%, 7.33%, and T-max by 10.5%, 11.5% for RCP 4.5 and RCP 8.5 respectively. Also, an increase in ETc of rice crop has been reported by 20%-22% while increment of wheat ETc has been noticed by 3%-4%. Thus, the proposed approach efficiently estimates ETc of wheat and rice crops using limited climate data and could assist water resource managers in achieving agricultural water sustainability.