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@article{206771,
        author = {Tejaswi Shriram Naik and Prof. Sanjay Kumar B M and Priyanka KP and Shruthi S Nayak and Sushmita D},
        title = {Detection of Black Pepper Adulteration Using Image Processing Method},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {401-408},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206771},
        abstract = {Black pepper is most widely used and valued spices worldwide, playing a significant role in culinary, medicinal, and industrial applications. Adulteration, which involves mixing black pepper with cheaper or inferior substances such as husks, stems, or other grains, significantly reduces quality and can potentially pose health risks. This project presents a reliable and automated approach to detect black pepper adulteration using advanced image processing techniques. High-resolution images of both pure and adulterated black pepper samples are captured under controlled conditions. Key features such as color variations, texture patterns, and shape characteristics are extracted and analyzed. the samples and accurately identify adulteration. The proposed method is non-destructive, rapid, and highly effective, offering practical benefits for spice manufacturers, retailers, and quality control laboratories. By providing an automated and precise solution, this study contributes to improving food safety, enhancing quality assurance processes, and ensuring consumer confidence in black pepper products.},
        keywords = {Black pepper, adulteration detection, image pro-cussing, feature extraction, machine learning, quality control, food safety, non-destructive analysis.},
        month = {July},
        }