Prediction of Environmental Parameters for Predatory Mite Cultivation Based on Temporal Feature Clustering

Abstract

With the significant annual increase in market demand for biopesticides, the industrial production demand for predatory mites, which hold the largest market share among biopesticides, has also been rising. To achieve efficient and low-energy consumption control of predatory mite breeding environmental parameters, accurate estimation of breeding environmental parameters is necessary. This paper collects and pre-processes hourly time series data on temperature and humidity from industrial breeding environments. Time series prediction models such as SVR, LSTM, GRU, and LSTNet are applied to model and predict the historical data of the breeding environment. Experiments validate that the LSTNet model is more suitable for such environmental modeling. To further improve prediction accuracy, the training data for the LSTNet model is enhanced using hierarchical clustering of time series features. After augmentation, the root mean square error (RMSE) of the temperature prediction decreased by 27.3%, and the RMSE of the humidity prediction decreased by 32.8%, significantly improving the accuracy of the multistep predictions and providing substantial industrial application value.