Abstract: A sugarcane yield prediction model based on intelligent algorithm was constructed to predict sugarcane yield in eight sugarcane production areas in Yunnan Province. Daily meteorological and soil data and yield data of Lincang, Dehong, Pu’er, Wenshan, Honghe, Baoshan, Xishuangbanna, and Yuxi of Yunnan Province for the period of 2000 to 2020 were selected, and the meteorological and soil factors that had a greater impact on the yield of sugarcane were preliminarily screened by the expert scoring method. Stepwise regression analysis algorithm was applied to screen the key influence factors of climate and soil during the growth cycle of sugarcane. Based on the division of the data set and the screening of the key influencing factors, a BP neural network yield prediction model was established with the annual meteorological and soil factors as the input variables and the annual sugarcane yield as the output variable. A Long Short-Term Memory （LSTM） neural network yield prediction model was developed using daily and annual meteorological and soil factors as input vectors and sugarcane yield as the output variable. The results of the test set showed that the coefficient of determination （R²） of the BP neural network model was 0.916, the mean absolute error （MAE） was 286 500 tons, and the root mean square error （RMSE） was 408 300 tons, and the R² of the LSTM neural network model was 0.978, the MAE was 160 400 tons, and the RMSE was 207 200 tons. The prediction accuracy of the LSTM neural network model was high, and the model performance was excellent and could better predict the sugarcane yield in Yunnan.

Key words: intelligent algorithm, sugarcane, BP neural network, long and short term memory network （LSTM） neural network, yield prediction, Yunnan Province