关键词: 酿酒葡萄霜霉病, 遗传算法, 长短期记忆神经网络, 时间序列

Abstract: With the largescale construction and introduction of vineyards in the eastern foothills of Helan Mountain in China, serious pest problems typified by downy mildew have emerged in each vineyard. In view of the lack of accurate prediction methods for downy mildew in wine grapes, a prediction model based on one kind of longterm and shortterm memory neural network which is improved by genetic algorithm is proposed. Genetic algorithm (GA) is added to the parameter adjustment link of the long shortterm memory neural network (LSTM) prediction model, and the optimal hyperparameter combination is obtained by continuously iterating in the hyperparameter search space through the optimization algorithm instead of manual parameter tuning. Then, the manual parameter LSTM model and BP neural network model based on downy mildewmeteorological time series data were established, and these three models were compared on the test set. The prediction results including root mean square error, mean square error, mean absolute error of GA-LSTM model are 0.410 3, 0.168 4 and 0.245 0 respectively, which are smaller than that of LSTM model and BP neural network model. The prediction results show that the prediction performance of LSTM in the application of time series problems is better than that of BP neural network model, by using genetic algorithm to optimize the hyperparameter selection link of LSTM model, the final hyperparameter combination is better than that obtained by manually tuned LSTM model.

Key words: wine grape downy mildew, genetic algorithm, long shortterm memory neural network, time series