<![CDATA[The integration of digital image processing and pattern recognition has opened new opportunities for improving agricultural product classification. This study focuses on the identification of three economically important ginger varieties red ginger, elephant ginger, and Emprit ginger through an image-based classification system. Unlike conventional manual inspection, which is prone to subjectivity and error, the proposed method applies a distance-based similarity measure to enhance consistency and reliability. Central to this approach is the use of the Manhattan Distance metric, chosen for its computational efficiency and robustness in high-dimensional data spaces. Two types of image features were explored: global intensity histograms and pixel vector representations. Comparative evaluation demonstrates that histogram-based classification achieves an accuracy of 86.6%, substantially outperforming the pixel vector approach at 76.6%. Novelty this research lies in demonstrating that lightweight, interpretable techniques can deliver competitive accuracy while avoiding the data and computational demands of more complex machine learning or deep learning models. This makes the system particularly suitable for smallholder farmers, local cooperatives, and resource-limited agricultural environments. Moreover, the study highlights the potential of histogram-based representation as a practical solution to variability in lighting and texture, offering improved robustness over traditional visual inspection or pixel-level methods. By contributing a simple yet effective framework, this research advances the field of agricultural informatics and supports the development of low-cost, automated tools for crop identification. Beyond academic significance, the findings have practical implications for supply chain management, post-harvest quality control, and precision agriculture, fostering transparency and value optimization in ginger production and distribution]]>
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