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Proceedings Paper

Image recognition of diseased rice seeds based on color feature
Author(s): Fang Cheng; Yibin Ying
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Paper Abstract

The objective of this research is to develop a digital image analysis algorithm for detection of diseased rice seeds based on color features. The rice seeds used for this study involved five varieties of Jinyou402, Shanyou10, Zhongyou207, Jiayou99 and IIyou3207. Images of rice seeds were acquired with a color machine vision system. Each original RGB image was converted to HSV color space and preprocessed to show, as hue in the seed region while the pixels value of background was zero. The hue values were scaled so that they varied from 0.0 to 1.0. Then six color features were extracted and evaluated for their contributions to seed classification. Determined using Blocks method, the mean hue value shows the strongest classification ability. Parzen windowing function method was used to estimate probability density distribution and a threshold of mean hue was drawn to classify normal seeds and diseased seeds. The average accuracy of test data set is 95% for Jinyou402. Then the feature of hue histogram was extracted for diseased seeds and partitioned into two clusters of spot diseased seeds and severe diseased seeds. Desired results were achieved when the two cancroids locations were used to discriminate the disease degree. Combined with the two features of mean hue and histogram, all seeds could be classified as normal seeds, spot diseased seeds and severe diseased seeds. Finally, the algorithm was implemented for all the five varieties to test the adaptability.

Paper Details

Date Published: 19 November 2004
PDF: 8 pages
Proc. SPIE 5587, Nondestructive Sensing for Food Safety, Quality, and Natural Resources, (19 November 2004); doi: 10.1117/12.570095
Show Author Affiliations
Fang Cheng, Zhejiang Univ. (China)
Yibin Ying, Zhejiang Univ. (China)


Published in SPIE Proceedings Vol. 5587:
Nondestructive Sensing for Food Safety, Quality, and Natural Resources
Yud-Ren Chen; Shu-I Tu, Editor(s)

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