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Identifying Rice Grains of Various Cultivars Using Machine Vision

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

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%.

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