Appearance quality grading for fresh corn ear using computer vision

被引：0
|
作者：
Wang H. [1 ]
Sun Y. [1 ]
Zhang T. [1 ]
Zhang G. [2 ]
Li Y. [3 ]
Liu T. [3 ]
机构：
[1] College of Biological and Agricultural Engineering, Jilin University
[2] College of Mechanical Science and Engineering, Jilin University
[3] Jilin Sky Scenery Food Co., Ltd.
来源：
Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery | 2010年 / 41卷 / 08期
关键词：
Appearance quality; Computer vision; Ear; Fresh corn; Grading; Neural network;
D O I：
10.3969/j.issn.1000-1298.2010.08.032
中图分类号：
学科分类号：
摘要：
Appearance quality grading for fresh corn ear was implemented by computer vision based on HSI color model. Bare tip position was detected and removed using projection method. Defects of fresh corn ear were identified by the first order differential operation on H. Characteristic parameters of appearance quality, such as defect proportion, ear length, ear maximum diameter, aspect ratio and rectangle factor were obtained. General regression neural network with five characteristic parameters as input was developed for grading. Experiment showed that average errors of bare tip position, ear length and ear maximum diameter were 2.27mm, 1.96mm and 0.54mm, respectively. Mistake rate of defect proportion was 3.00%, and grading average ratio was up to 95.91%.
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页码：156 / 159+165
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