Reinventing Spice Authentication: Merging Artificial Intelligence Insights with Traditional Methods for Authentication of Cardamom

Spices have one of the significant impact on mankind examining its historical, cultural, economic, and health importance. This research article shed light on the pressing problem of spice adulteration, with a specific emphasis on the difficulties encountered in case of cardamom, often referred to as the "Queen of Spices." The paper highlight the absence of a strong digital authentication system for spices and suggest a new way that utilizes artificial intelligence andmachine learning to authenticate spices, particularly cardamom. This paper presents the establishment of a machine learning-based digital model for identifying cardamom. The approach involves creating a thorough dataset, preprocessing the data, and using transfer learning with the MobileNet model. The performance examination of the model demonstrates its efficacy in precisely detecting cardamom and its adulterants with accuracy of 95.5%, underscoring its appropriateness for low-power devices. The paper analyzes the visual distinctions between biological adulterants, namely Citrus sinensis and Amomum subulatum, and highlight the significance of color and surface characteristics in the process of authentication. The article also provides a comprehensive overview of industrial methods used to detect impurities in both whole and ground cardamom. The paper emphasizes the need of integrating cutting-edge technology with conventional approaches to ensure the quality of cardamom in the spice sector.