基于稀疏表示算法的高通量玉米果穗粒型识别系统

玉米籽粒粒型是评估玉米产量和品质的重要表型参数之一,为了提高籽粒粒型的识别率,同时满足高通量以及无损测量的要求,该文以果穗整体为研究对象,基于稀疏表示的方法构建了高通量玉米果穗籽粒粒型识别系统(果穗未脱粒).以掉落抓拍法硬件采集平台采集3种不同粒型(硬粒型、马齿型、半马齿型)的玉米穗图像,首先使用帧差法获取果穗轮廓,再通过G通道分离、OTSU算法(最大类间方差法)得到籽粒轮廓信息,提取籽粒部分颜色、形状、纹理特征作为分类依据,每种粒型取200粒作为训练样本构成稀疏表示算法的判别字典,对每一个测试样本计算稀疏表示系数,根据最小重构误差判定籽粒粒型类别.结果表明,该方法不需要传统的果穗脱粒再进行籽...

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Bibliographic Details
Published in农业工程学报 Vol. 33; no. z1; pp. 219 - 224
Main Author 马钦 王越 郭浩 朱德海 刘哲 张晓东 李绍明
Format Journal Article
LanguageChinese
Published 农业部农业信息获取技术重点实验室,北京 100083%中国农业大学信息与电气工程学院,北京,100083 2017
中国农业大学信息与电气工程学院,北京 100083
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ISSN1002-6819
DOI10.11975/j.issn.1002-6819.2017.z1.033

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Summary:玉米籽粒粒型是评估玉米产量和品质的重要表型参数之一,为了提高籽粒粒型的识别率,同时满足高通量以及无损测量的要求,该文以果穗整体为研究对象,基于稀疏表示的方法构建了高通量玉米果穗籽粒粒型识别系统(果穗未脱粒).以掉落抓拍法硬件采集平台采集3种不同粒型(硬粒型、马齿型、半马齿型)的玉米穗图像,首先使用帧差法获取果穗轮廓,再通过G通道分离、OTSU算法(最大类间方差法)得到籽粒轮廓信息,提取籽粒部分颜色、形状、纹理特征作为分类依据,每种粒型取200粒作为训练样本构成稀疏表示算法的判别字典,对每一个测试样本计算稀疏表示系数,根据最小重构误差判定籽粒粒型类别.结果表明,该方法不需要传统的果穗脱粒再进行籽粒类型统计,识别正确率达到94.8%,测量速度达到28穗/min,大大提高了玉米粒型统计的效率.
Bibliography:11-2047/S
identification;computer vision;image processing;sparse representation;maize grain type;high throughput
Maize grain type is one of important phenotype parameters to evaluate maize yield and quality. In order to improve the recognition rate of the maize grain type and achieve high throughput and nondestructive measurement, the maize grain type identification system based on sparse representation is established. For the maize ear's irregular shape and uneven growth, the phenotypic trait acquisition of ear needs to meet the all-dimensional requirements. The hardware acquisition equipment is designed to capture vertically dropping maize ear by 3 high-speed cameras crossed with an angle of 120° mutually in black box. The ear falls down very fast during the process, and thus the high speed of camera's shutter is needed. In addition, enough supplemental lighting is essential because of the high speed of camera. The size of black box is 800 mm × 800 mm × 350 mm. The high-speed CCD (charge coupled dev
ISSN:1002-6819
DOI:10.11975/j.issn.1002-6819.2017.z1.033