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Computer and Computing Technologies in Agriculture IX 9th IFIP WG 5.14 International Conference, CCTA 2015 Beijing, China, September 27–30, 2015, Part I |
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Conference papers
Rapid Identification of Rice Varieties by Grain Shape and Yield-Related Features Combined with Multi-class SVM
Abstract : Rice is the major food of approximately half world population and thousands of rice varieties are planted in the world. The identification of rice varieties is of great significance, especially to the breeders. In this study, a feasible method for rapid identification of rice varieties was developed. For each rice variety, rice grains per plant were imaged and analyzed to acquire grain shape features and a weighing device was used to obtain the yield-related parameters. Then, a Support Vector Machine (SVM) classifier was employed to discriminate the rice varieties by these features. The average accuracy for the grain traits extraction is 98.41 %, and the average accuracy for the SVM classifier is 79.74 % by using cross validation. The results demonstrated that this method could yield an accurate identification of rice varieties and could be integrated into new knowledge in developing computer vision systems used in automated rice-evaluated system.
Cited literature [16 references]
https://hal.inria.fr/hal-01557805
Contributor : Hal Ifip <>
Submitted on : Thursday, July 6, 2017 - 3:49:49 PM
Last modification on : Friday, July 17, 2020 - 7:06:03 PM
Long-term archiving on: : Wednesday, January 24, 2018 - 1:56:34 AM
Contributor : Hal Ifip <>
Submitted on : Thursday, July 6, 2017 - 3:49:49 PM
Last modification on : Friday, July 17, 2020 - 7:06:03 PM
Long-term archiving on: : Wednesday, January 24, 2018 - 1:56:34 AM
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