Image recognition of diseased rice seeds based on color feature

Fang Cheng; Yibin Ying

doi:10.1117/12.570095

19 November 2004 Image recognition of diseased rice seeds based on color feature

Fang Cheng, Yibin Ying

Proceedings Volume 5587, Nondestructive Sensing for Food Safety, Quality, and Natural Resources; (2004) https://doi.org/10.1117/12.570095
Event: Optics East, 2004, Philadelphia, Pennsylvania, United States

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.

Citation Download Citation

Fang Cheng and Yibin Ying "Image recognition of diseased rice seeds based on color feature", Proc. SPIE 5587, Nondestructive Sensing for Food Safety, Quality, and Natural Resources, (19 November 2004); https://doi.org/10.1117/12.570095

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