基于比重和图像的核桃内部品质无损检测与分选
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国家自然科学基金项目(52075003、61379080)和山西省回国留学人员科研教研项目(HGKY2019065)


Internal Quality Nondestructive Detection and Sorting Principle of Walnut Based on Density and Digital Image
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    摘要:

    针对核桃内部品质不易检测、市场上带壳核桃中劣质核桃比例较高的问题,阐述了根据比重判断核桃内部品质的原理,提出核桃无损检测与分选方案。将摄像工具所采集的核桃图像进行处理、拟合,并估算核桃体积;由称量平台获取核桃质量并换算成重量;根据核桃的比重使不同核桃落入不同容器中,或根据核桃冲击力与核桃体积的比值,即比重等效参数对核桃进行检测与分选。在核桃下落到平面时,核桃中分面与平面的夹角相对稳定在60°左右,利用这一特点设置正面、侧面图像采集工具,可较准确地获取核桃的轮廓,从而提高分选精度。该方法可间接检测核桃内部品质,对核桃壳仁、工作环境、操作人员均无不良影响。

    Abstract:

    Because the internal quality of walnuts is not easy to detect and a certain proportion of shelled walnuts in supermarkets are of poor quality, the principle of judging the internal quality of walnut according to the density was discussed, and the method and equipment of walnut sorting were studied. The principle of sorting walnuts was as follows: firstly, the walnut image collected by the camera was processed to estimate the volume of walnut;the weight of walnut was obtained;the gas pipe was selected according to the density of walnut to output gas;as a result, different walnuts fell into different containers. Walnut can also be detected and sorted according to density equivalent parameters. Secondly, when the walnut fell to the platform, the angle between its split plane and the platform was always about 60°. With this feature, the front and side cameras can be set in the correct orientation to obtain the contours of walnut, calculate the density of walnut and improve the sorting accuracy. In conclusion, this technology can indirectly detect the internal quality of walnut and promote intelligent and advanced detection. The sorting scheme had no adverse effect on walnut, working environment and operators. With the decrease of camera price, the decline of image processing cost and the improvement of computer operation speed, the cost of walnut sorter would be greatly reduced. The food processing plant would be able to arrange multiple work stations and multiple sorters at the same time, so as to meet the requirements of production efficiency when large quantities of walnut were sorted. Compared with the existing methods, the proposed method can realize nondestructive detection and had high academic value.

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于大国,赵明,SHI Yijun,陈俐华,曹成茂,王俊元.基于比重和图像的核桃内部品质无损检测与分选[J].农业机械学报,2021,52(7):373-378,357. YU Daguo, ZHAO Ming, SHI Yijun, CHEN Lihua, CAO Chengmao, WANG Junyuan. Internal Quality Nondestructive Detection and Sorting Principle of Walnut Based on Density and Digital Image[J]. Transactions of the Chinese Society for Agricultural Machinery,2021,52(7):373-378,357

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  • 收稿日期:2021-01-28
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  • 在线发布日期: 2021-07-10
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