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Citrus is an important fruit and it’s strongly relevant between quality and irrigation. Traditional irrigation strategies relying on human experience caused two problems, i.e. inaccurate irrigation timing and quantity. Both of the two problems have negative influence on citrus. The evapotranspiration of citrus orchard is an important index of water consumption. In order to evaluate citrus orchard evapotranspiration (ET) to make more scientific and precise irrigation strategies, the long shortterm memory (LSTM), extreme learning machine (ELM) and general regression neural network (GRNN) methods were applied to model ET and test its performance based on climatic data. The result showed that LSTM performed the best in mean absolute error (MAE) and root mean square error (RMSE) than the other two models. And ELM model performed closely to GRNN. In order to evaluate the certainty of three models, the Monte Carlo analysis method was added to the process of training. The result indicated that LSTM had good accuracy in different input features while ELM tended to overestimate ET and GRNN tended to underestimate ET. It’s practical to applicate the proposed method to make precise irrigation strategies. 