The ability to meet the demand for healthy dried grains by consumers is a great motivation for industrial grain processors to ensure grain quality retention during the drying process. The use of non-destructive, timely, accurate, reliable, economical, and environment-friendly techniques for grain quality detection remains in the top interest of researchers and agricultural industries in recent years. Physical and sensory inspection as well as physicochemical index analysis is part of the manual approaches for measuring grain quality. Due to sample integrity destruction and low efficiency, these procedures struggle to satisfy modern standards of their high susceptibility to human and analytical errors. The rapid advancement in measurement methods has led to the use of a variety of optical-based techniques for monitoring grain throughout the drying process since they are non-destructive and highly effective. The techniques such as RGB imaging, micro imaging, and thermal imaging have been extensively used to assess grain qualities which include grain size, shape, shrinkage, colour, temperature distribution, and microstructure. Meanwhile, the application of optical spectroscopy such as ultraviolet–visible (UV–Vis), near-infrared (NIR), and shortwave infrared (SWIR) spectral in grain drying were less evaluated in recent years. Therefore, it is crucial to understand the recent advances in the principle, procedure, and application of non-destructive optical imaging techniques for assessing grain qualities during the drying process. This requires attention for the development of improved monitoring and controlling system for the grain drying process, to ensure timely, healthy and high-quality dried grain production. •Optical imaging techniques are widely used in grain drying.•RGB imaging, micro imaging, and thermal imaging have been used extensively to assess grain quality.•Imaging technology has proven its reliability in monitoring grain quality during the drying process.•Integration with artificial intelligence promotes automation in the drying process.