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基于计算机视觉的花生品质分级检测研究
引用本文:韩仲志,赵友刚.基于计算机视觉的花生品质分级检测研究[J].中国农业科学,2010,43(18):3882-3891.
作者姓名:韩仲志  赵友刚
作者单位:(青岛农业大学理学与信息学院)
基金项目:山东省自然科学基金,山东省科技攻关项目,青岛市科技发展计划项目 
摘    要:【目的】建立能够对花生进行品质分级的计算机视觉无损检测方法。【方法】同步拍摄和扫描11类品质,每类品质100颗和100宗,每宗100颗不同等级的花生籽粒的正反面图像;参照国家标准量化花生品质籽粒的11个限制性检测项目,设计花生规格和品质等级的判别方法;测量每个籽粒的形态、纹理、颜色共3大类54个外观特征,采用主分量分析(PCA)进行特征优化,构建并比较BP神经网络(ANN)和支持向量机(SVM)品质检测模型;分别应用Matlab和Spss工具软件实现检测过程和对结果进行统计分析。【结果】前16个主分量的SVM模型,能够鉴别95%以上的不完善粒、霉变、杂质、异品种等不同品质的籽粒,与人工检测结果吻合度达到了93%,对100宗待检样品进行检测,规格和等级检测完全正确率达到了92%。【结论】研究结果为花生的品质分级检测提供了比较系统全面的量化标准和检测方法,该方法可推广应用于花生品质鉴别、分级筛选加工和商品分级定价等领域。

关 键 词:花生仁  计算机视觉  品质分级  无损检测
收稿时间:2010-02-10;

Quality Grade Detection in Peanut Using Computer Vision
HAN Zhong-zhi,ZHAO You-gang.Quality Grade Detection in Peanut Using Computer Vision[J].Scientia Agricultura Sinica,2010,43(18):3882-3891.
Authors:HAN Zhong-zhi  ZHAO You-gang
Affiliation:(Department of Science and Information, Qingdao Agricultural University)
Abstract:【Objective】 The objective of this study is to establish a kind of quality nondestructive testing method, which can be used for grading peanut quality, based on computer vision. 【Method】 Digital color image of peanuts were taken and scanned from 2 side faces each of 100 kernels each of 11 different kinds of quality and of 100 kernels each of 100 groups. Referring to national standards of China, 11 restrictive items of peanuts kernels of different kinds of quality have been devised and quantized. Also the distinguishable methods of size and grades of peanuts have been devised. Fifty-four appearance characters belonging to 3 categories of shape, color and texture had been measured. And then the characters were optimized based on PCA. ANN and SVM quality testing models were built and compared. Using MATLAB and SPSS, the results were analyzed. 【Result】 The SVM model based on the first 16 PCs could detect at 95% accuracy different qualities of unsound/mildew/impurity/different peanut varieties. Also these results fitted at 93% accuracy close to that of tested by manual. By testing 100 groups of peanuts, the correct rate of size and grade was 92%.【Conclusion】 The result of this study has provided a new method which can be used in peanut quality testing and grade testing, and this method is good and stable. This method can be generalized and used in peanuts testing of different qualities, grade screening, processing, and commodity grading and pricing.
Keywords:peanut kernel  computer vision  quality grades  nondestructive detection
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