Prediction of Multiple Environmental Factors in Laying Hen Houses Using AKF-Attention-CNN-LSTM

Accurate prediction of multiple environmental factors in laying hen houses is essential for maintaining optimal rearing conditions, yet the strong nonlinear coupling among temperature, relative humidity, CO2, and PM2.5 concentrations, combined with complex temporal dependencies, poses significant challenges for conventional forecasting models. To address these challenges, this study proposes a collaborative multi-environmental factor prediction framework that integrates adaptive Kalman filtering (AKF), a dynamic attention mechanism, and a convolutional neural network–long short-term memory (CNN-LSTM) architecture. The AKF preprocesses raw sensor data to suppress noise while preserving salient transient features; the attention mechanism adaptively weights time steps exhibiting rapid changes across environmental variables; and the CNN-LSTM model performs synchronized high-precision forecasting of all target factors. Experimental validation using real-world summer data from commercial layer hen houses demonstrates that the proposed model achieves superior predictive accuracy across all four environmental variables, effectively capturing both short-term fluctuations and long-term temporal dependencies. These findings indicate that the AKF-Attention-CNN-LSTM framework can serve as a reliable tool for precise environmental regulation, providing actionable forecasts to support automated climate control in laying hen production systems.