Computer vision inspection of rice seed quality with discriminant analysis

Fang Cheng; Yibin Ying

doi:10.1117/12.570143

25 October 2004 Computer vision inspection of rice seed quality with discriminant analysis

Fang Cheng, Yibin Ying

Proceedings Volume 5608, Intelligent Robots and Computer Vision XXII: Algorithms, Techniques, and Active Vision; (2004) https://doi.org/10.1117/12.570143
Event: Optics East, 2004, Philadelphia, Pennsylvania, United States

Abstract

This study was undertaken to develop computer vision-based rice seeds inspection technology for quality control. Color image classification using a discriminant analysis algorithm identifying germinated rice seed was successfully implemented. The hybrid rice seed cultivars involved were Jinyou402, Shanyou10, Zhongyou207 and Jiayou99. Sixteen morphological features and six color features were extracted from sample images belong to training sets. The color feature of 'Huebmean' shows the strongest classification ability among all the features. Computed as the area of seed region divided by area of the smallest convex polygon that can contain the seed region, the feature of 'Solidity' is prior to the other morphological features in germinated seeds recognition. Combined with the two features of 'Huebmean' and 'Solidity', discriminant analysis was used to classify normal rice seeds and seeds germinated on panicle. Results show that the algorithm achieved an overall average accuracy of 98.4% for both of normal seeds and germinated seeds in all cultivars. The combination of 'Huebmean' and 'Solidity' was proved to be a good indicator for germinated seeds. The simple discriminant algorithm using just two features shows high accuracy and good adaptability.

Citation Download Citation

Fang Cheng and Yibin Ying "Computer vision inspection of rice seed quality with discriminant analysis", Proc. SPIE 5608, Intelligent Robots and Computer Vision XXII: Algorithms, Techniques, and Active Vision, (25 October 2004); https://doi.org/10.1117/12.570143

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KEYWORDS

Machine vision

Inspection

Computer vision technology

Detection and tracking algorithms

Algorithm development

RGB color model

Cameras

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