基于机器视觉的马铃薯加工原料分选系统

目的：设计以马铃薯加工原料为对象的自动化分选系统，实现设定标准下马铃薯自动识别。方法：构建分选系统的控制流程及分选算法，通过自动传送、机器视觉采集、吸压翻转自动化获取马铃薯2面的图像，采用图像复原算法消除运动模糊，设计面积比、长短径、凸起检测算法对马铃薯畸形、发芽、大小进行检测，基于颜色特征构建神经网络模型对马铃薯绿皮、病斑、常色进行分类。结果：利用BP神经网络算法预测马铃薯外观颜色绿皮、病斑、正常的分类，以误差分数为衡量预测模型准确性的度量，神经网络的预测分类平均准确率为96.2%。通过选取混合样本对分选系统进行测试，参照设定分选标准，分选系统对马铃薯识别正确率达到95.92%；单薯处理耗时3.76 s。系统运行稳定。结论：该方法用于马铃薯加工原料精量分选可行，能够满足薯制品加工生产线前端的分选需要。

Research on separation system of potato processing raw materials based on machine vision

Objective: Taking raw potatoes as the objects, an automatic sorting system was designed to realize the automatic identification of potato under the set standard, which provided technical support for the processing and production of potato products. Methods: The control flows of sorting system and sorting algorithms were constructed. Images of two sides of a potato were acquired automatically through automatic transmission, machine vision acquisition and suction pressure turning. The image restoration algorithms were used to eliminate motion blur and the detection algorithms of area ratio, length diameter and bulge were designed to detect potatoes deformity, germination and size of potatoes. A neural network model was established based on color features to classify green skin, discoloration and normal color of potatoes. Results: The BP neural network algorithm was used to predict the appearance color class of green skin, disfigured spots and normal. The average accuracy of prediction classification of neural network is 96.2% by measuring the prediction model with error score. The sorting system was tested by selecting mixed samples. Referring to the sorting standard, the identification accuracy of potatoes reached 95.92% and the processing time of a single potato is 3.76 s. The system runs stably. Conclusion: The method is feasible for precise sorting of raw potato as processing materials, which meets the needs of sorting potatoes in the front end of processing line.