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Proceedings Paper

Image-processing algorithms for inspecting characteristics of hybrid rice seed
Author(s): Fang Cheng; Yibin Ying
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Paper Abstract

Incompletely closed glumes, germ and disease are three characteristics of hybrid rice seed. Image-processing algorithms developed to detect these seed characteristics were presented in this paper. The rice seed used for this study involved five varieties of Jinyou402, Shanyou10, Zhongyou207, Jiayou and IIyou. The algorithms were implemented with a 5*600 images set, a 4*400 images set and the other 5*600 images set respectively. The image sets included black background images, white background images and both sides images of rice seed. Results show that the algorithm for inspecting seeds with incompletely closed glumes based on Radon Transform achieved an accuracy of 96% for normal seeds, 92% for seeds with fine fissure and 87% for seeds with unclosed glumes, the algorithm for inspecting germinated seeds on panicle based on PCA and ANN achieved n average accuracy of 98% for normal seeds, 88% for germinated seeds on panicle and the algorithm for inspecting diseased seeds based on color features achieved an accuracy of 92% for normal and healthy seeds, 95% for spot diseased seeds and 83% for severe diseased seeds.

Paper Details

Date Published: 30 March 2004
PDF: 9 pages
Proc. SPIE 5271, Monitoring Food Safety, Agriculture, and Plant Health, (30 March 2004); doi: 10.1117/12.516046
Show Author Affiliations
Fang Cheng, Zhejiang Univ. (China)
Yibin Ying, Zhejiang Univ. (China)


Published in SPIE Proceedings Vol. 5271:
Monitoring Food Safety, Agriculture, and Plant Health
George E. Meyer; Yud-Ren Chen; Shu-I Tu; Bent S. Bennedsen; Andre G. Senecal, Editor(s)

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