Click on “Download PDF” for the PDF version or on the title for the HTML version.

If you are not an ASABE member or if your employer has not arranged for access to the full-text, Click here for options.

Abstract. Fissures in rice kernels developed prior to harvest and post-harvest processing significantly reduce head rice yield, a crucial parameter for evaluating rice quality and economic value in the rice industry. In this study, fissures in rough rice were revealed by scanning using an X-ray system. An algorithm was developed using an open source programming language python and “OpenCV” library to detect and measure fissures in rough rice kernels from the X-ray images. This algorithm can automatically segment and measure the size of rice kernels in the X-ray image. The algorithm detects fissures by adaptive thresholding of individual rice kernel and passing through series of filters. Fissure parameters (location, direction, and dimensions of fissures) of individual rice kernels were measured. Statistics of fissure parameters for rice kernels were produced for each sample of X-ray images. This algorithm demonstrated very good repeatability for fissure detection and measurement. The relative standard deviation was less than 10% for repeated measurement of fissure parameters. The accuracy of developed algorithm was validated by visual inspection of rough rice with deviation of less than 2% in percentage of kernels fissured. The developed fissure detection and measurement algorithm provides a useful tool for quantifying fissures in rough rice samples. This information could be used to assess fissure formation and development in rough rice during processing and storage, as well as in estimating fissuring levels and head rice yield for rough rice samples without a cumbersome milling process.