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.

借助认知计算模型可以推进灌溉实践。反复干旱、人口扩张和全球变暖的影响共同对灌溉实践施加了严格的限制。参考蒸散量 (ET ₀ ) 是根据气候数据预测作物需水量的重要因素。有许多技术可用于预测 ET ₀。高效的 ET ₀预测模型在灌溉系统中发挥重要作用，可提高水生产力。在本研究中，对用于估计 ET _{0 的}认知计算模型进行了回顾. 评论表明人工神经网络 (ANN) 方法优于支持向量机 (SVM) 和遗传编程 (GP)。二阶神经网络 (SONN) 是 ANN 模型中最有前途的方法。