吴彦红,刘木华,杨君,郑华东.基于计算机视觉的大米外观品质检测[J].农业机械学报,2007,38(7):107-111.
.[J].Transactions of the Chinese Society for Agricultural Machinery,2007,38(7):107-111.
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基于计算机视觉的大米外观品质检测   [下载全文]
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DOI:10.3969/j.issn.1000-1298.[year].[issue].[sequence]
中文关键词:  大米  品质  检测  计算机视觉
基金项目:
吴彦红  刘木华  杨君  郑华东
江西农业大学
中文摘要:开发了一套基于计算机视觉技术的稻谷品质检测系统,采用灰度变换、自动阈值分割、区域标记等方法从采集的稻米群体图像中提取单体米粒图像,对单体米粒的裂纹、垩白特征进行了统计和检测方法研究。提取了米粒的面积、周长等10 个特征参数作为整精米检测特征,并进行了主成分分析,确定了判别整精米的优化阈值。检测试验结果表明:裂纹米粒识别的准确率为96.41 %;垩白米粒识别的准确率为94.79%;整精米识别的准确率为
Key Words:
Abstract:96.20%。 The quality of rice is the main factor that affects the market price of rice. Today, the detecting and grading of rice are mainly carried out by manual ways, which is time-consuming and toilful, and even easily leads to improper judgment. A detecting system of rice quality based on computer vision was developed in this paper. The methods that segmenting single kernel from mass rice image using gray transformation, automatic threshold segmentation, and region marking were discussed. In order to detect the head rice ratio, ten parameters were selected from the profile of rice kernels, such as the area and perimeter of rice kernel, the two axes of the equivalent oval, the inspection of the profile of rice kernel and head rice rate were discussed after using the principal components of the profile parameters of rice kernel. The results of detecting experiments on five varieties of rice indicated that the accurate ratio of detecting fissure is about 96.41%, the accurate ratio of chalkiness detecting is about 94.79%, and the correct ratio of detecting head rice is about 96.20%.

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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