| Summary: | The absence of accurate measurement or calculation techniques for crop water requirements in greenhouses frequently results in over- or under-irrigation. In order to find a better method, this study analyzed the accuracy, data consistency and practicability of the Penman–Monteith (PM), Hargreaves–Samani (HS), Pan Evaporation (PAN), and Artificial Neural Network (ANN) models. Model-calculated crop evapotranspiration (ET<sub>C</sub>) was compared with lysimeter-measured crop evapotranspiration (ET<sub>C</sub>) in the National Precision Agriculture Demonstration Station in Beijing, China. The results showed that the actual ET<sub>C</sub> over the entire experimental period was 176.67 mm. The ET<sub>C</sub> calculated with the PM, HS, PAN, and ANN model were 146.07 mm, 189.45 mm, 197.03 mm, and 174.7 mm, respectively, which were different from the actual value by −17.32%, 7.23%, 11.52%, and −1.12%, respectively. The order of the calculation accuracy for the four models is as follows: ANN model > PAN model > PM model > HS model. By comprehensively evaluating the statistical indicators of each model, the ANN model was found to have a significantly higher calculation accuracy compared to the other three models. Therefore, the ANN model is recommended for estimating ET<sub>C</sub> under greenhouse conditions. The PM and PAN models can also be used after improvement.
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