Time series prediction of insect pests in tea gardens.

These findings suggest that different prediction lengths influence the model performance in tea garden pest time series prediction. Additionally, deep learning could be applied satisfactorily in predicting time series of insect pests in tea gardens based on LSTM-attention. Thus, this study provides a theoretical basis for the research on the time series of pest and disease infestations in tea plants. This article is protected by copyright. All rights reserved.