Integration of deep learning and IIoT for automated soil moisture management in precision agriculture

The rapid evolution of Industry 4.0 has led to the adoption of Industrial Internet of Things (IIoT) in various sectors, including manufacturing and smart agriculture. The integration of real‐time monitoring, predictive analytics, and intelligent decision‐making has transformed the large‐scale smart agriculture into a data‐driven and automated industrial process and aligned it with Industry 4.0 principles. This study presents an IIoT‐enabled predictive analytics framework for smart agriculture providing real‐time soil moisture monitoring—a critical parameter for optimizing large‐scale agricultural production and industrial farming processes. The proposed system collects real‐time environmental data using sensor networks, processes it through cloud‐based big data frameworks (Apache Kafka, MongoDB, Apache Spark), and applies a hybrid deep learning model (long short‐term memory‐gate recurrent unit [LSTM‐GRU]) to provide real‐time predictive decision‐making. Soil moisture is measured as volumetric water content (%), and all prediction errors are reported in the same unit. The proposed hybrid LSTM‐GRU model achieved a root mean squared error of 0.412% compared to 0.418% for LSTM, reducing prediction error by approximately 1.6%. Similarly, mean squared errors were reduced from 0.175 and 0.169, representing an improvement of nearly 3.2%, and the hybrid model achieved a higher coefficient of determination ( R 2 ) of 0.99785 compared to 0.99778 for LSTM, indicating an improvement in overall model fit. These results demonstrate a marginal but consistent improvement in predictive performance achieved by the hybrid architecture. This automated decision‐making and intelligent resource management aligns the proposed system with Industry 4.0's vision of interconnected, autonomous, and self‐optimizing industrial systems.