Identifying rice grains using image analysis and sparse representation based classification
A microscope system was developed for acquiring high resolution grain images of 30 rice varieties.The morphological, textural, and color traits of the rice grains were quantified using image processing.Trait discrepancies among varieties were observed and explained.Sparse representation based classifier was developed to identify the varieties of the grains.The classification achieved an accuracy of 89.1% and a standard deviation of 7.0% Rice (Oryza sativa L. is a major staple food worldwide, and is traded extensively. The objective of this study is to distinguish the rice grains of 30 varieties nondestructively using image processing and sparse representation based classification (SRC) SRC uses over complete bases to capture the representative traits of rice grains. In the experiments, rice grain images were acquired by microscopy. The morphological, color, and textural traits of the grain body, sterile lemmas, and brush were quantified. An SRC classifier was subsequently developed to identify the varieties of the grains using the traits as the inputs. The proposed approach could discriminate rice grain varieties with an accuracy of 89.1%
Comput. Electron. Agric.