张帆,李绍明,刘哲,朱德海,王越,马钦.基于机器视觉的玉米异常果穗筛分方法[J].农业机械学报,2015,46(S1):45-49.
Zhang Fan,Li Shaoming,Liu Zhe,Zhu Dehai,Wang Yue,Ma Qin.Screening Method of Abnormal Corn Ears Based on Machine Vision[J].Transactions of the Chinese Society for Agricultural Machinery,2015,46(S1):45-49.
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基于机器视觉的玉米异常果穗筛分方法   [下载全文]
Screening Method of Abnormal Corn Ears Based on Machine Vision   [Download Pdf][in English]
投稿时间:2015-10-28  
DOI:10.6041/j.issn.1000-1298.2015.S0.008
中文关键词:  玉米异常果穗  机器视觉  筛分  图像处理
基金项目:公益性行业科研专项资金资助项目(201203026)和中央高校基本科研业务费专项资金资助项目(2015XD003)
作者单位
张帆 中国农业大学 
李绍明 中国农业大学 
刘哲 中国农业大学 
朱德海 中国农业大学 
王越 中国农业大学 
马钦 中国农业大学 
中文摘要:针对玉米品种制种过程中病害果穗的表型识别问题,以玉米果穗整体为研究对象,基于二维快速成像技术实现了霉变、虫蛀和机械损伤3种异常果穗的快速分选。构建了单目视觉便携式图像采集装置,采集了任意摆放的粘连果穗目标图像,分别在RGB模型和HIS模型中提取了玉米果穗的6个颜色特征和5个纹理特征,并实现特征参数的归一化。构建了病害果穗分类模型,并采用已知样本特征向量对支持向量机和BP神经网络方法进行训练和对比分析,最后采用支持向量机方法实现了3种异常果穗的快速分选。实验结果表明,该方法对霉变异常果穗筛分的正确率可达96.0%,虫蛀果穗筛分的正确率可达93.3%,机械损伤果穗筛分的正确率可达90.0%。
Zhang Fan  Li Shaoming  Liu Zhe  Zhu Dehai  Wang Yue  Ma Qin
China Agricultural University,China Agricultural University,China Agricultural University,China Agricultural University,China Agricultural University and China Agricultural University
Key Words:Abnormal corn ears  Machine vision  Screening  Image processing
Abstract:The quality of corn seed production and new variety breeding are affected by the problem of abnormal corn ears. Taking the whole corn ear as research object, the sorting method of three abnormal grains (namely moldy corn ears, worm-eaten corn ears and mechanically damaged corn ears) was researched based on two-dimensional fast imaging technology. Firstly, the portable image acquisition device was constructed based on the monocular vision and the corn ear image was acquired. According to these characteristics of corn ear images, six color features in RGB model and HIS model and five texture features in gray scale images were extracted and normalized to build the classification model of these abnormal corn ears. The classifiers were trained with the support vector machine (SVM) and BP neural network for comparison analysis by using the known feature vectors. The result showed that the SVM classifier had higher accuracy than BP neural network classifier. The accuracies of moldy corn ears sorting, worm-eaten corn ears sorting and mechanically damaged corn ears sorting were 96.0%, 93.3% and 90.0%, respectively. The study made an important foundation for realizing the automatic machine screening of abnormal corn ears and had high application value in improving the corn seed quality.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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