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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. Identifying Rice Grains of Various Cultivars Using Machine VisionPublished by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org Citation: 2015 ASABE Annual International Meeting 152188279.(doi:10.13031/aim.20152188279)Authors: Tzu-Yi Kuo, Szu-Yu Chen, Heng-An Lin, Chia-Lin Chung, Yan-Fu Kuo Keywords: Rice cultivar, support vector machine, principle component analysis, grain shape, grain color Abstract. The demand for food authentication is increasing worldwide in recent years. Rice (Oryza sativa L.) is a staple food and is traded globally in great amount. This study proposed an image-based method for discriminating rice grains of 45 cultivars. Images of rice grains were taken by using a digital camera. Morphological, color, and sterile lemma traits of the grains were quantified using image processing algorithms. The traits were analyzed by principle component analysis to investigate the discrepancies among the cultivars. Support vector machines were then developed to identify the cultivars of the grains by using the traits as the inputs. The overall accuracy of the classifiers achieved 85.02%. (Download PDF) (Export to EndNotes)
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