| 基于机器视觉的双孢蘑菇在线自动分级系统设计与试验 |
| |
| 引用本文： | 王风云,封文杰,郑纪业,孙家波,牛鲁燕,陈振学,张学涛,王磊.基于机器视觉的双孢蘑菇在线自动分级系统设计与试验[J].农业工程学报,2018,34(7):256-263. |
| |
| 作者姓名： | 王风云 封文杰 郑纪业 孙家波 牛鲁燕 陈振学 张学涛 王磊 |
| |
| 作者单位： | 1. 山东省农业科学院科技信息研究所，济南 250100；,1. 山东省农业科学院科技信息研究所，济南 250100；,1. 山东省农业科学院科技信息研究所，济南 250100；,1. 山东省农业科学院科技信息研究所，济南 250100；,1. 山东省农业科学院科技信息研究所，济南 250100；,2. 山东大学控制科学与工程学院，济南 250061,2. 山东大学控制科学与工程学院，济南 250061,1. 山东省农业科学院科技信息研究所，济南 250100； |
| |
| 基金项目： | 山东省重点研发计划（2016GNC110008）；山东省农业科学院农业科技创新工程（CXGC2017B04）；山东省农业科学院农业科技创新工程（CXGC2016A12） |
| |
| 摘 要： | 针对双孢蘑菇工厂化生产中人工分级劳动量大、生产效率低、标准不统一等问题,该文研究设计了一套基于机器视觉的双孢蘑菇精选分级系统,提出基于分水岭、Canny算子、闭运算等处理的双孢蘑菇图像大小分级算法,设计了基于传送速度、距离、触发时间与算法处理时间的精确控制策略,开发了基于Open CV 2.4.10和visual studio 2010的系统分析与控制软件,在最大限度减少破损情况下,实现双孢蘑菇实时在线精选分级。基于研发的双孢蘑菇自动精选分级系统样机,对新鲜双孢蘑菇进行了分级性能及分级效果的测试。试验结果表明,在输送速度12.7 m/min、相机行频1 900 Hz下,自动分级系统的平均分级速度是102.41个/min、平均准确率97.42%、破损率0.05%、漏检率0.96%,相对于人工分级效率提高38.86%,准确率提高6.84%,破损率降低0.13%,可以连续稳定工作。对于长时间分级,由于人容易疲劳,自动分级的优势更加明显。
|
| 关 键 词： | 图像处理 菌 算法 双孢蘑菇 自动分级 |
| 收稿时间： | 2017/11/2 0:00:00 |
| 修稿时间： | 2018/1/22 0:00:00 |
Design and experiment of automatic sorting and grading system based on machine vision for white agaricus bisporus |
| |
| Wang Fengyun,Feng Wenjie,ZhengJiye,Sun Jiabo,Niu Luyan,Chen Zhenxue,ZhangXuetao and Wang Lei.Design and experiment of automatic sorting and grading system based on machine vision for white agaricus bisporus[J].Transactions of the Chinese Society of Agricultural Engineering,2018,34(7):256-263. |
| |
| Authors: | Wang Fengyun Feng Wenjie ZhengJiye Sun Jiabo Niu Luyan Chen Zhenxue ZhangXuetao and Wang Lei |
| |
| Institution: | 1. S&T Information Institution, Shandong Academy of Agricultural Sciences, Jinan 250100, China;,1. S&T Information Institution, Shandong Academy of Agricultural Sciences, Jinan 250100, China;,1. S&T Information Institution, Shandong Academy of Agricultural Sciences, Jinan 250100, China;,1. S&T Information Institution, Shandong Academy of Agricultural Sciences, Jinan 250100, China;,1. S&T Information Institution, Shandong Academy of Agricultural Sciences, Jinan 250100, China;,2. Control Science and Engineering School, Shandong University, Jinan 250061, China,2. Control Science and Engineering School, Shandong University, Jinan 250061, China and 1. S&T Information Institution, Shandong Academy of Agricultural Sciences, Jinan 250100, China; |
| |
| Abstract: | Abstract: White Agaricus bisporus is an excellent source of the B vitamins, riboflavin, niacin, and pantothenic acid and also a good source of the dietary mineral phosphorus. It is one of the most commonly and widely consumed mushrooms in the world. The production of white Agaricus bisporus has been industrialized in China. However, during the last production chain, it needs a lot of labors to sort and grade the white Agaricus bisporus. The manual sorting and grading mode has many disadvantages such as larger error, low productivity, non-uniform standard, and so on. With the development of machine vision technology, it has been successfully used for automatic inspection and sorting, especially in agricultural industry due to its nondestructive characteristic. An automatic sorting and grading system based on machine vision was designed in this paper. Firstly, an automatic sorting and grading hardware system was designed. It included conveying mechanism, image acquiring system, control module and actuator. The conveying mechanism consists of the fixed support, conveyor, roller, driving unit, tension unit, cleaner and guide plate. The image acquiring system consists of a line scan camera, lens, light source and its controller. The control module consists of photoelectric sensor controlling the camera, electromagnetic valve and relay starter controlling the actuator. The actuator consists of the air compressor, duplex pieces, muffler, air cylinder, joints, gas channel, guide rod, slider, connector, flap, baffle, and so on. Secondly, it put forward an image algorithm based on the watershed method, Canny operator, OR operation and closed operation to determine the diameter of white Agaricus bisporus. The first watershed algorithm combining the global threshold segmentation method and maximum entropy threshold segmentation method is used to remove the shadow of image. The second watershed algorithm based on Canny operator, OR operator and closed operation is used to remove the disturbance of petiole. The minimum enclosing rectangle method is used to get the diameter of white button mushroom. Thirdly, a precise control strategy based on the conveyor speed, distance between trigger and flap piece, trigger time and algorithm processing time was designed. Finally, the software based on OpenCV 2.4.10 and Visual Studio 2010 was developed in this paper to acquire, analyze and process the image and output the control instruction to control the valve by USB-4761 module. It also has the visual parameter configuration function for camera, control module and grading standard. When the whole system starts at the first time, the light source of image acquiring system is adjusted by the light controller to make the image clear and stable. The air compressor is powered on and pressurized to the rated pressure of 0.3 MPa. The motor of conveyor is started and the speed of the conveyor belt is adjusted according to the actual production requirement. The white Agaricus bisporus goes into the guide bar with the conveyor belt. When the white Agaricus bisporus goes into the region of image acquiring, it triggers the industrial camera to scan. The image data are transmitted to the industrial computer by image capture card. The software analyzes the image on line. The analyzed result is sent to the related solenoid valve through the digital control module. When the white Agaricus bisporus arrives near the container of related grade, it is sorted into the container by the related flap piece driven by the solenoid valve. In order to validate the applicability and reliability, the test of grading performance and effect was carried out with the prototype of sorting and grading system at 12.7 m/min conveying speed and 1900 Hz line frequency. The results showed that the average maximum grading speed was 102.41 pieces/min, the accuracy of grading was 97.42%, the damage rate was 0.05% and the undetected rate was 0.96%. The grading speed improved by 38.86%, the accuracy improved by 6.84% and the damage rate reduced by 0.13% compared to the manual grading. The system can stably and continuously operate. For long time grading, the advantage of intelligent system is more obvious due to the fatigue of labor. The whole system realizes the on-line automatically sorting and grading for fresh white Agaricus bisporus with the minimum destruction. |
| |
| Keywords: | image processing bacteria algorithms agaricus bisporus automatic grading |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《农业工程学报》浏览原始摘要信息 |
| 点击此处可从《农业工程学报》下载免费的PDF全文 |
|
