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Sorting System Development of Potato Blackheart Based on Light Transmission Imaging

机译:基于透光成像的马铃薯黑钟分拣系统开发

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Potato blackheart is a kind of internal physiological disease which couldn't be found by people from the surface. In order to identify the potato blackheart without destruction and more accurately, a sorting system which could collect the light transmission images of potato tubers and automatically detect and sort the potatoes with blackheart was developed. The light sources used in this research were light emitting diodes and the wavelength was 705 nm. The blackheart part of potato tuber could weaken the transmitted light more effectively than the normal part, forming a dark-gray spot in the transmission image that could be detected by the computer image processing algorithm. During the image preprocessing period, the potato image area was extracted from the background, while median filtering method was used to reduce noise in the image. After that, the blackheart part ofpotato or other low grayscale area was separated based on the grayscale histogram. The parameters, namely the average grayscalevalue of the whole potato image area, the low grayscale area and the high grayscale area, the standard deviation of the whole potato image area, the low grayscale area and the high grayscale area were calculated based on the processed image. 72 potatoeswere selected to validate the property of the system, and they were divided into a calibration group and a validation group according to the ratio of 3:1. After image preprocessing, MLR discriminant method was used to build a model to identify the samples. Eventually, approximately 89% of all the samples were detected and diverted accurately. The result showed that the nondestructive sorting system developed in this study could be used to detect the potatoes with blackheart accurately.
机译:马铃薯黑鹰是一种内部生理疾病,无法从表面上发现。为了在没有破坏的情况下识别马铃薯黑色,并且更准确地,可以采集马铃薯块茎的透光图像并自动检测并将马铃薯与黑色的分类进行分类。本研究中使用的光源是发光二极管,波长为705nm。马铃薯块茎的黑色部分可以比正常部分更有效地削弱透射光,在可以通过计算机图像处理算法检测的透射图像中的暗灰度点。在图像预处理期间,从背景中提取马铃薯图像区域,而中值滤波方法用于降低图像中的噪声。之后,基于灰度直方图分离波形的Blackheart部分或其他低灰度区域。参数,即整个马铃薯图像区域,低灰度区域和高灰度区域的平均灰度值,整个马铃薯图像区域的标准偏差,低灰度区域和高灰度区域是基于处理的图像计算的。 72个PotaOswere选择验证系统的属性,并根据3:1的比例分为校准组和验证组。在图像预处理之后,使用MLR判别方法来构建模型以识别样品。最终,检测到所有样品的约89%并准确转移。结果表明,本研究开发的非破坏性分拣系统可用于准确地检测黑钟的土豆。

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