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Abstract
The integration of Internet of Things (IoT) technology into industrial settings has significantly transformed various sectors by automating processes and enhancing decision support systems, thereby boosting productivity and efficiency in agricultural production. This study proposes a Stochastic Petri Net (SPN) model to assess the performance of smart agricultural industrial facilities that integrate Edge, Fog, and Cloud Computing technologies. These technologies utilize sensors to monitor critical operational parameters such as temperature, humidity, and equipment status, enabling efficient data collection, processing, and analysis for informed decision-making and improved operational efficiency. Key challenges include managing large data volumes and ensuring timely data transfer between computing layers, impacting real-time poultry monitoring. The SPN model evaluates key performance metrics, including response time, resource utilization, discard probability, and throughput, while optimizing parameters to enhance system performance and further the application of IoT in industrial automation.