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@article{206688,
        author = {Sanika K M and Sudarshan K and Poorvitha and Rashmitha Shetty and Surabhi A R},
        title = {Rice Quality Analysis: A Comprehensive Review of Image Processing and Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {215-218},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206688},
        abstract = {Rice quality assessment can be defined as the task of determining the physical, chemical, and biological attributes of rice kernels. Several variables affect the quality of rice kernels and therefore must be evaluated to ensure proper production, storage, and utilization of this grain. Traditional methods of rice quality analysis rely mostly on destructive testing which makes the process costly, time-consuming, and prone to subjective results. Modern technology allows developing new, non-destructive techniques that require minimal human interaction and provide precise results in shorter time periods. This paper focuses on the development of different types of quality assessment systems and their application for the evaluation of rice. Modern techniques can be divided into two categories based on the type of parameters to be determined. One category involves the analysis of physical characteristics of rice such as size, weight, shape, color, presence of cracks, and chalkiness level. This kind of assessment is conducted using computer vision systems based on advanced image processing and machine learning algorithms. Another category of modern rice quality assessment techniques includes non-destructive spectroscopic measurements for determination of chemical characteristics of rice kernels. This approach allows analyzing the internal structure of grains to obtain grains are collected from different resources.},
        keywords = {Rice Quality, Digital Image Processing, Machine Learning, Deep Learning, Spectroscopy, Hyperspectral Imaging, Non-Destructive Testing.},
        month = {July},
        }