Daily reference evapotranspiration prediction of Tieguanyin tea plants based on mathematical morphology clustering and improved generalized regression neural network

Tieguanyin tea plant is the most important tea cultivar in Fujian Province, China. It has suffered great economic losses due to high temperature and dry weather in recently years. This study proposed a prediction model for daily reference evapotranspiration (ET0) of Tieguanyin based on the combination of mathematical morphology clustering (MMC) and generalized regression neural networks (GRNN). Average air temperature, sunshine hours and relative humidity were chosen as the input factors of GRNN after a correlation analysis of the microclimate factors of the tea garden. The MMC was adopted to cluster the historical meteorological data to find the similar class as the training dataset of GRNN. The fruit fly optimization algorithm (FOA) was used to optimize the smoothing factor of GRNN. The meteorological data from JAN. 2018 to OCT. 2019 measured in Dabaofeng tea garden, Anxi County, Fujian Province, China were used to train and test the proposed model, and the performances was assessed with RMSE, MAE and model validity coefficient. The prediction results of different seasons show that the proposed model is efficient with high accuracy and has good adaptability under complex meteorological conditions.