30 March 2004 Image-processing algorithms for inspecting characteristics of hybrid rice seed
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Proceedings Volume 5271, Monitoring Food Safety, Agriculture, and Plant Health; (2004); doi: 10.1117/12.516046
Event: Optical Technologies for Industrial, Environmental, and Biological Sensing, 2003, Providence, RI, United States
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.
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Fang Cheng, Yibin Ying, "Image-processing algorithms for inspecting characteristics of hybrid rice seed", Proc. SPIE 5271, Monitoring Food Safety, Agriculture, and Plant Health, (30 March 2004); doi: 10.1117/12.516046; https://doi.org/10.1117/12.516046
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KEYWORDS
Inspection

Algorithm development

Machine vision

Detection and tracking algorithms

Cameras

RGB color model

Error analysis

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