A review of digital twin applications for optimizing grain drying: Challenges and opportunities

In the face of global food security challenges and the continuously increasing pressure of drying energy consumption, problems such as uneven grain quality, local overheating and excessive humidity, and low energy efficiency caused by opaque processes and reliance on experience-based operations in traditional grain drying technologies have become increasingly prominent. Digital twin technology, as a new paradigm of dynamic interaction and mapping between virtual and real Spaces, is providing a revolutionary solution to the predicament of grain drying. This article systematically expounds the core composition, current development status and inherent limitations of digital twin technology, deeply analyzes its key role in predictive maintenance and product optimization, and compares the fundamental differences in its application paradigms in the industrial and agricultural fields. By comprehensively reviewing the research and application of digital twin technology in the field of grain drying, this paper demonstrates that as a key enabling technology, it can achieve visual monitoring, precise regulation and dynamic optimization of the drying process through the deep integration of Internet of Things perception, multi-scale modeling and intelligent algorithms. Research shows that the in-depth application of this technology can not only effectively solve the pain points such as uneven temperature and humidity distribution and excessive reliance on experience in traditional drying, but also significantly improve the quality of grains (such as reducing cracks and retaining nutrients), and at the same time achieve personalized process customization and a significant reduction in energy consumption, thereby strongly promoting the digital transformation of the grain drying industry toward high efficiency, low carbon and intelligence.