Abstract
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.
Publication Date
9-28-2021
First Page
139
Last Page
144
DOI
10.13652/j.issn.1003-5788.2021.09.023
Recommended Citation
Ming, LI; Run-tao, WANG; and Wei, JIANG
(2021)
"Research on separation system of potato processing raw materials based on machine vision,"
Food and Machinery: Vol. 37:
Iss.
9, Article 23.
DOI: 10.13652/j.issn.1003-5788.2021.09.023
Available at:
https://www.ifoodmm.cn/journal/vol37/iss9/23
References
[1] 李小昱, 陶海龙, 高海龙, 等. 马铃薯缺陷透射和反射机器视觉检测方法分析[J]. 农业机械学报, 2014, 45（5）: 191-196.
[2] 金瑞, 李小昱, 颜伊芸, 等. 于高光谱图像和光谱信息融合的马铃薯多指标检测方法[J]. 农业工程学报, 2015, 31（16）: 258-263.
[3] 田海韬, 赵军, 蒲富鹏. 马铃薯芽眼图像的分割与定位方法[J]. 浙江农业学报, 2016, 28（11）: 1 947-1 953.
[4] 邓立苗, 杜宏伟, 徐艳, 等. 基于机器视觉的马铃薯智能分选方法与实现[J]. 中国农机化学报, 2015, 36（5）: 145-150.
[5] 王红军, 熊俊涛, 黎邹邹, 等. 基于机器视觉图像特征参数的马铃薯质量和形状分级方法[J]. 农业工程学报, 2016, 32（8）: 272-277.
[6] 田芳, 彭彦昆, 魏文松, 等. 基于机器视觉的马铃薯黑心病检测机构设计与试验[J]. 农业工程学报, 2017, 33（5）: 287-294.
[7] 张擞. 基于图像处理技术测量马铃薯表形特征[J]. 物理实验, 2017, 37（7）: 53-55.
[8] 向静, 何志良, 汤林越, 等. 结合计算机视觉的马铃薯外部品质检测技术[J]. 计算机工程与应用, 2018, 54（5）: 165-169.
[9] 李玉华, 李天华, 牛子孺, 等. 基于色饱和度三维几何特征的马铃薯芽眼识别[J]. 农业工程学报, 2018, 34（24）: 158-164.
[10] 许伟栋, 赵忠盖. 基于PCA-SVM算法的马铃薯形状分选[J]. 控制工程, 2020, 27（2）: 246-252.
[11] 王润涛, 李明, 龚振平. 基于双目视觉的球形果实单体采摘终端装置研究[J]. 食品与机械, 2018, 34（7）: 93-100.
[12] 李明, 王润涛, 赵忠媛. 马铃薯自动选种催芽系统研究[J]. 吉林农业大学学报, 2019, 41（4）: 489-494.
[13] 芦范. 基于改进神经网络算法的蔬菜图像识别[J]. 食品与机械, 2020, 36（2）: 146-150.
[14] 邓羽翔, 罗诚, 李东亮. 基于ＢＰ神经网络的烟叶醇化感官质量仿真模拟[J]. 食品与机械, 2020, 36（3）: 161-165.
[15] 秦正龙, 冯长君. 基于神经网络的菠萝香气成分色谱保留值研究[J]. 食品与机械, 2020, 36（1）: 30-33.