Abstract
Forecasting crop pest outbreaks under conditions of increasing agroclimatic variability is a critical task for intelligent decision support systems in agriculture. Traditional statistical and empirical models typically have limited transferability and insufficient accuracy when describing nonlinear and multiscale relationships between climatic factors and pest population dynamics. This paper proposes a hybrid algorithm combining wavelet analysis and deep learning methods for forecasting agroclimatic pest infestation levels. The algorithm is based on multiscale decomposition of time series using a discrete wavelet transform, after which the extracted components are used as input features for a deep neural network implementing a nonlinear mapping between climatic parameters and infestation indicators. The developed computational framework includes the stages of data preprocessing, feature space formation, model training, and forecast generation in a single, reproducible pipeline. An experimental evaluation using long-term agroclimatic and phytosanitary data showed that the proposed algorithm outperforms classical regression and individual neural network models in terms of RMSE, MAE, and the coefficient of determination. The results confirm the effectiveness of integrating wavelet analysis and deep learning for developing phytosanitary risk forecasting algorithms and demonstrate the potential of the proposed approach for implementation in intelligent precision farming systems.