Ma, Y.; Lin, H.; Chen, W.; Chen, W.; Wang, Q. Prediction of Environmental Parameters for Predatory Mite Cultivation Based on Temporal Feature Clustering. Preprints2024, 2024081180. https://doi.org/10.20944/preprints202408.1180.v1
APA Style
Ma, Y., Lin, H., Chen, W., Chen, W., & Wang, Q. (2024). Prediction of Environmental Parameters for Predatory Mite Cultivation Based on Temporal Feature Clustering. Preprints. https://doi.org/10.20944/preprints202408.1180.v1
Chicago/Turabian Style
Ma, Y., Weijie Chen and Qianyan Wang. 2024 "Prediction of Environmental Parameters for Predatory Mite Cultivation Based on Temporal Feature Clustering" Preprints. https://doi.org/10.20944/preprints202408.1180.v1
Abstract
With the significant annual increase in the 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-processing hourly time-series data of temperature and humidity from industrial breeding environments. Time series prediction models such as SVR, LSTM, GRU, and LSTNet are applied to model and predict 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 temperature prediction decreased by 27.3%, and the RMSE of humidity prediction decreased by 32.8%, significantly improving the accuracy of multi-step predictions and providing substantial industrial application value.
Keywords
time series clustering, LSTNet, greenhouse environmental parameter modeling, temperature and humidity prediction
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
