<![CDATA[Fluorescence imaging has developed as a promising non-invasive method for identifying aflatoxin contamination in agricultural commodities, especially corn kernels. This paper examines current improvements in fluorescence imaging technologies, highlighting its potential to improve food safety through swift and precise detection of mycotoxins. The paper examines the basics of fluorescence, the necessary setup for optimal imaging, and the issues related to background fluorescence interference, sensitivity, and the construction of calibration models. Although there are some limitations, fluorescence imaging presents considerable advantages, such as cost-efficiency and the capacity to obtain concurrent spectral and spatial data. Proposed future research objectives include the validation of imaging systems using naturally contaminated samples, the optimization of imaging parameters, and the integration of machine learning techniques to enhance data processing. By overcoming existing constraints and utilizing technical progress, fluorescence imaging can serve as an essential instrument in the detection of aflatoxin contamination, hence enhancing food safety. Keywords: Aflatoxin, Detection, Fluorescence imaging, Food safety, Machine learning.]]>
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