Cognitive computing models for estimation of reference evapotranspiration: A review

Irrigation practices can be advanced by the aid of cognitive computing models. Repeated droughts, population expansion and the impact of global warming collectively impose rigorous restrictions over irrigation practices. Reference evapotranspiration (ET0) is a vital factor to predict the crop water requirements based on climate data. There are many techniques available for the prediction of ET0. An efficient ET0 prediction model plays an important role in irrigation system to increase water productivity. In the present study, a review has been carried out over cognitive computing models used for the estimation of ET0. Review exhibits that artificial neural network (ANN) approach outperforms support vector machine (SVM) and genetic programming (GP). Second order neural network (SONN) is the most promising approach among ANN models.

Automated summary

This study discusses the use of cognitive computing models for estimating ET0, with results showing that artificial neural network (ANN) approach outperforms others, in which second order neural network (SONN) is the most promising.