Smart drying: Current status, challenges, and future trends

Abstract

This review examines the evolving landscape of smart drying technologies, with an emphasis on recent advances, persistent challenges, and emerging trends. Conventional drying methods, such as sun and hot air drying, have gradually been replaced by advanced techniques, including drying via microwaving, freezing, and heat pumps, each offering specific benefits but also notable limitations related to energy efficiency, product quality, and process control. In response, smart drying technologies have emerged, integrating sensor systems, automation, and artificial intelligence to enable real-time monitoring and adaptive control of drying operations. This review synthesizes current developments in smart drying tools, including computer vision, electronic aroma sensing, hyperspectral imaging, nuclear magnetic resonance, and near-infrared spectroscopy, which enable the continuous assessment of critical parameters such as temperature, humidity, moisture distribution, and aroma evolution. The reported outcomes demonstrate improvements in drying efficiency, energy reduction, and preservation of product quality. However, their widespread adoption remains constrained by the high implementation costs and the need for customized sensing strategies tailored to diverse food matrices. Recent trends highlight the increasing application of machine learning and deep learning models for process-related prediction, optimization, and fault diagnosis, supported by adaptive control systems and cloud-based monitoring platforms. Overall, smart drying represents a transformative approach with substantial potential across the agriculture, food processing, pharmaceutical, and materials sectors. By critically synthesizing technological progress, the implementation barriers, and future development pathways, this review provides a strategic reference for researchers, policymakers, and industry stakeholders seeking to advance scalable, sustainable, and intelligent drying systems for industrial applications.

Keywords

Smart Drying / Sensors / Artificial Intelligence / Post-harvest losses / Policymaking

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