<![CDATA[Closed-type poultry houses facilitate consistent output by ensuring a steady microenvironment conducive to optimal avian growth. Nevertheless, numerous farms continue to depend on manual oversight of temperature, humidity, and ammonia levels, resulting in delayed reactions, diminished productivity, and heightened environmental stress on poultry. These constraints underscore the necessity for predictive and automated systems that can monitor and forecast environmental variables in real time. Prior research indicates that LSTM networks are proficient in nonlinear time-series forecasting nonetheless, when used in isolation, LSTM models encounter difficulties in capturing linear seasonal patterns and long-term trends present in chicken house environmental data. This research presents a hybrid forecasting framework that combines LSTM and SARIMA models to concurrently represent nonlinear temporal dependencies and linear seasonal components. Environmental metrics such as temperature, soil moisture, and ammonia concentration were acquired using SHT31, Soil Moisture, and MQ137 sensors, processed using a Python-Flask backend, saved in MongoDB, and visualized through a cross-platform Flutter-based web interface. Experimental findings indicate that the proposed LSTM–SARIMA model exhibits robust predictive efficacy, with MAE = 0.62, MSE = 0.55, RMSE = 0.58, MAPE = 7.89%, and R² = 0.86. The findings demonstrate that the suggested method efficiently facilitates early warning systems and real-time microclimate evaluation, allowing for expedited environmental management measures and minimizing production losses due to unstable poultry house conditions.]]>
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