The milled whole kernel rice is passed through a rice grader to remove defects. The rice is passed before an electronic eye that detects any difference in color. A jet of air removes defects. Usually damaged kernels (with black specks), yellow kernels, and stones are removed. The very best mills pass the rice through two breaks of sorters. Rice graders can be adjusted to remove green immature kernels from brown rice, chalky rice from white (more translucent kernels) rice, or regular medium grain rice from chalky mochi rice. There are special sorters to remove glass from rice. Milled rice is passed through different type of sieve to take out broken rice.
In the developed system, first, images of milled rice samples are captured. The image processing operations are then executed to eliminate undesirable noises from images. After kernel segmentation, a primary feature vector is created based on some shape, size and color features. To have a high classification accuracy, it is necessary to prepare a proper input vector for the classifiers. For this purpose, the primary extracted features are subjected to a correlation-based feature selection procedure to reject the inferior features. Finally, the best classifier is selected for milled rice grading by examining four commonly used metaheuristic approaches. The entire applied methodology is described in the following sections.
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