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Image Processing Method for Embedded Optical Peanut Sorting

机译:嵌入式光学花生分选的图像处理方法

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摘要

Sorting of finished products or agriculture food has different method for ultra high speed quality inspection. Optical sorting is one of the important applications of image processing used in industries to replace manual method to verify quality of finished products or row food. Most of the systems use the computer as main processing device that perform image processing algorithms on it, such kind of system having limitations like higher cost, bigger size and large Initial boot-up time. This type of design cannot be implemented for ultra fast, higher capacity and smaller in size agricultural products like nuts, grains and pulses. Standalone image processing have embedded image processing platform that can able to overcome the limitation of computer based systems at certain level. As peanuts (Arachis hypogeal) come from farm, they are mixed with foreign material like rocks, moisture contended soil particles and outer shells of raw peanuts and they must be separated with high level of accuracy and precision. here discussed the multi channel peanut sorting algorithm that apply on raspberry pi ARM platform for peanut quality segregation by sort out foreign material as well as defective peanut like aflatoxin contaminants and fungi allergies contents from the required quality good peanuts. In paper we discuss about implementation of such a system by using conveyor belt method and image processing algorithm. Algorithm takes consider the color and size of peanut for optical peanut sorting process.
机译:成品或农业食品的分类有不同的超高速质量检查方法。光学分拣是图像处理的重要应用之一,在工业中用于替代手动方法以验证成品或行式食品质量的图像处理。大多数系统将计算机用作在计算机上执行图像处理算法的主要处理设备,这种系统具有诸如较高的成本,较大的尺寸和较大的初始启动时间等限制。这种类型的设计无法用于超快速,高容量和较小尺寸的农产品,如坚果,谷物和豆类。独立图像处理具有嵌入式图像处理平台,该平台可以在一定程度上克服基于计算机的系统的局限性。由于花生(花生)是从农场来的,因此它们混有异物,例如岩石,潮湿的土壤颗粒和生花生的外壳,因此必须进行高精度和高精度的分离。本文讨论了在树莓派ARM平台上应用的多通道花生分选算法,通过从所需的优质花生中分选异物以及有缺陷的花生(如黄曲霉毒素)和真菌过敏成分,对花生进行质量分离。在本文中,我们讨论了使用传送带方法和图像处理算法实现这种系统的方法。该算法在光学花生分选过程中考虑了花生的颜色和大小。

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