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Automated strawberry grading system based on image processing

机译:基于图像处理的草莓自动分级系统

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Using machine-vision technology to grade strawberries can increase the commercial value of the strawberry. The automated strawberry grading system has been set up based on three characteristics: shape, size and colour. The system can efficiently obtain the shape characteristic by drawing the lines and then class with K-means clustering method for the strawberry image. The colour of the strawberry adopts this Dominant Colour method into the a* channel, and the size is described by the largest fruit diameter. The strawberry automated grading system can use one, two or three characteristics to grade the strawberry into three or four grades. In order to solve the multicharacteristic problems, the multi-attribute Decision Making Theory was adopted in this system. The system applied a conveyer belt, a camera, an image boxn two photoelectrical sensors, a leading screw driven by a motor, a gripper, two limit switches and so onn The system was controlled by the single-chip-microcomputer (SCM) and a computer. The results show that the strawberry size detection error is not more than 5%, the colour grading accuracy is 88.8%, and the shape classification accuracy is above 90%. The average time to grade one strawberrv is below 3 s.
机译:使用机器视觉技术对草莓进行分级可以提高草莓的商业价值。草莓自动分级系统已根据以下三个特征进行了设置:形状,大小和颜色。该系统可以通过绘制线条并使用K均值聚类方法对草莓图像进行分类来有效地获得形状特征。草莓的颜色在a *通道中采用这种主导颜色方法,其大小由最大的果实直径描述。草莓自动分级系统可以使用一,二或三个特征将草莓分级为三或四个等级。为了解决多特征问题,该系统采用了多属性决策理论。该系统应用了一条传送带,一个照相机,一个图像盒,两个光电传感器,一个由马达驱动的导螺杆,一个夹具,两个限位开关等。该系统由单片机(SCM)和一个电脑。结果表明,草莓尺寸检测误差不超过5%,颜色分级精度为88.8%,形状分类精度为90%以上。一年级秸秆分类的平均时间少于3秒。

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