Grain drying is a critical post-harvest operation that determines storage stability, quality preservation, and economic value of harvested cereals and legumes. Conventional drying methods often suffer from inefficient energy utilization, inconsistent quality outcomes, and limited real-time monitoring capabilities, leading to substantial post-harvest losses and increased operational costs. This article examines the integration of Cyber-Physical Systems (CPS) in automated grain drying processes to address these challenges through intelligent sensor-actuator networks, adaptive control algorithms, and data-driven decision-making frameworks. CPS architectures combine embedded computing, communication networks, and physical process control to enable real-time moisture and temperature regulation, optimize energy consumption, and maintain grain quality parameters. Key components include distributed sensor networks for environmental monitoring, programmable logic controllers (PLCs) for process automation, and cloud-based analytics platforms for predictive modeling and optimization. Applications of CPS-enabled grain drying demonstrate significant improvements in energy efficiency (20-40% reduction), drying uniformity, and reduction of thermal damage to grain kernels. The integration of Internet of Things (IoT) technologies and machine learning algorithms further enhances system adaptability and fault detection capabilities. Despite implementation challenges including initial capital costs and technical expertise requirements, CPS-based automation represents a transformative approach toward sustainable, quality-preserving, and economically viable grain post-harvest management in modern agricultural systems.Grain drying is a critical post-harvest operation that determines storage stability, quality preservation, and economic value of harvested cereals and legumes. Conventional drying methods often suffer from inefficient energy utilization, inconsistent quality outcomes, and limited real-time monitoring capabilities, leading to substantial post-harvest losses and increased operational costs. This article examines the integration of Cyber-Physical Systems (CPS) in automated grain drying processes to address these challenges through intelligent sensor-actuator networks, adaptive control algorithms, and data-driven decision-making frameworks. CPS architectures combine embedded computing, communication networks, and physical process control to enable real-time moisture and temperature regulation, optimize energy consumption, and maintain grain quality parameters. Key components include distributed sensor networks for environmental monitoring, programmable logic controllers (PLCs) for process automation, and cloud-based analytics platforms for predictive modeling and optimization. Applications of CPS-enabled grain drying demonstrate significant improvements in energy efficiency (20-40% reduction), drying uniformity, and reduction of thermal damage to grain kernels. The integration of Internet of Things (IoT) technologies and machine learning algorithms further enhances system adaptability and fault detection capabilities. Despite implementation challenges including initial capital costs and technical expertise requirements, CPS-based automation represents a transformative approach toward sustainable, quality-preserving, and economically viable grain post-harvest management in modern agricultural systems.