基于机器视觉的穴盘烟苗自动间苗算法研究
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摘要
【目的】为了满足烟苗快速、准确、自动间苗的要求,提出了基于机器视觉的穴盘烟苗自动间苗算法研究,避免传统的人眼观察效率低、随意性大等缺陷。【方法】采用基于Lab空间下的K-means聚类彩色图像分割算法,根据矩阵行列求和法,在两峰之间取像素坐标,定位其区域位置,把穴盘分割成128个单元格,将目标区域转化为二值图像。通过分别比较烟苗的圆形度、长宽比和矩形度,发现这3种成分的同一形态特征有一定差异,可作单、多株及空穴烟苗识别参数。利用幼苗植株的面积和周长,在不同生长期设定一个合适阈值,实现自动间苗的目的。【结果】仿真和数据分析表明:取圆形度1.256 6、长宽比1.571 4、矩形度0.716 5的区分效果最好。取面积分布在111～243 (像素),周长分布在16～33 (像素)可以判定为壮苗。在MATLAB R2015a环境下开发了机器视觉烟苗自动间苗软件系统。【结论】烟苗数目正确识别率达到97.04%以上,空穴位置达到100%,间苗位置及壮苗识别平均准确率分别达到94.76%和89.58%,为进一步开发基于机器视觉的烟苗自动间苗机提供了理论基础和技术支持。
[Purpose]In order to meet the requirements of fast, accurate and automatic thinning of tobacco seedlings, we put a forward automatic tray seedling thinning algorithm based on machine vision. It can avoid the shortcomings of the low efficiency, arbitrariness of the traditional human eye observation.[Methods]K-means clustering was used to image segmentation of tobacco seedlings in Lab color space, according to the matrix row sum method, draw pixel coordinates between the two peaks, locate its the region position, divide the plug into 128 cells, convert the target area to a binary image. Research indicated that each kind of shape feature such as roundness, aspect ratio and rectangularity had different values and could be used as the separating parameters by comparing each feature respectively for the three ingredients, which are the single, multi-plant and holes. The use of seedling plants area and perimeter, in different growth period to set a suitable threshold to achieve the purpose of automatic seedling.[Results]The simulation data and analysis showed the roundness of1.256 6, aspect ratio of 1.571 4, rectangular of 0.716 5 the best difference between the effect. Taking the area distribution in the 111-243(pixels), the circumference of the distribution in the 16-33(pixels)can be determined as strong seedlings. We developed a tobacco seedling automatic thinning software system based on machine vision on the MATLAB R2015 a environment.[ Conclusion] The result showed that the correct identification rate of tobacco seedling had reached more than 97.04%,the hole position had reached 100%, for the thinning position and sound seedling average rate were respectively 94.76% and 89.58%, this method provided a theoretical basis and technical support for the further development of the automatic seedling thinning machine based on machine vision.
[Purpose]In order to meet the requirements of fast, accurate and automatic thinning of tobacco seedlings, we put a forward automatic tray seedling thinning algorithm based on machine vision. It can avoid the shortcomings of the low efficiency, arbitrariness of the traditional human eye observation.[Methods]K-means clustering was used to image segmentation of tobacco seedlings in Lab color space, according to the matrix row sum method, draw pixel coordinates between the two peaks, locate its the region position, divide the plug into 128 cells, convert the target area to a binary image. Research indicated that each kind of shape feature such as roundness, aspect ratio and rectangularity had different values and could be used as the separating parameters by comparing each feature respectively for the three ingredients, which are the single, multi-plant and holes. The use of seedling plants area and perimeter, in different growth period to set a suitable threshold to achieve the purpose of automatic seedling.[Results]The simulation data and analysis showed the roundness of1.256 6, aspect ratio of 1.571 4, rectangular of 0.716 5 the best difference between the effect. Taking the area distribution in the 111-243(pixels), the circumference of the distribution in the 16-33(pixels)can be determined as strong seedlings. We developed a tobacco seedling automatic thinning software system based on machine vision on the MATLAB R2015 a environment.[ Conclusion] The result showed that the correct identification rate of tobacco seedling had reached more than 97.04%,the hole position had reached 100%, for the thinning position and sound seedling average rate were respectively 94.76% and 89.58%, this method provided a theoretical basis and technical support for the further development of the automatic seedling thinning machine based on machine vision.
引文
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[18]杨帆.数字图像处理及应用(MATLAB版)[M].北京:化学工业出版社,2013.
[2]师会勤.我国烤烟育苗的几种主要方式探讨[J].南昌高专学报,2005,20(1):100.DOI:10.3969/j.issn.1008-7354.2005.01.037.
[3]杨怀千.烤烟漂湿育苗的研究及配套技术的推广[D].长沙:湖南农业大学,2010.
[4]魏星,吴华建.不同育苗方式对烟苗质量及烟叶产质量的影响[J].陕西农业科学,2011,57(2):77.DOI:10.3969/j.issn.0488-5368.2011.02.030.
[5]徐文兵.粉煤灰替代烟草育苗基质中草炭的研究[D].长沙:湖南农业大学,2014.
[6]杨飞,祝诗平,邱青苗.基于计算机视觉的花椒外观品质检测及其MATLAB实现[J].农业工程学报,2008,24(1):198.DOI:10.3321/j.issn:1002-6819.2008.01.039.
[7]CHEN Y R,CHAO K L,KIM S.Machine vision technology for agricultural applications[J].Computers and Electronics in Agriculture,2002,36:173.
[8]应义斌,饶秀勤,赵匀,等.机器视觉技术在农产品品质自动识别中的应用(Ⅰ)[J].农业工程学报,2000,16(1):103.DOI:10.3321/j.issn:1002-6819.2000.01.001.
[9]肖超云.基于机器视觉的杂草识别研究[D].镇江:江苏大学,2007.
[10]贺鹏,黄林.植物叶片特征提取及识别[J].农机化研究,2008(6):168.DOI:10.3969/j.issn.1003-188X.2008.06.050.
[11]吴兰兰.基于数字图像处理的玉米苗期田间杂草的识别研究[D].武汉:华中农业大学,2010.
[12]阚江明,王怡萱,杨晓微,等.基于叶片图像的植物识别方法[J].科技导报,2010,28(23):81.
[13]贾洪雷,王刚,郭明卓,等.基于机器视觉的玉米苗植株数量获取方法与试验[J].农业工程学报,2015,31(3):215.
[14]庄珍珍,祝诗平,孙雪剑,等.基于机器视觉的烟叶自动分级方法研究[J].西南师范大学学报,2016,41(4):123.
[15]孙明,凌云.基于计算机视觉的萝卜幼苗自动识别技术[J].农业机械学报,2002,33(5):75.DOI:10.3969/j.issn.1000-12982002.05.023.
[16]蒋焕煜,施经挥,任烨,等.机器视觉在幼苗自动移钵作业中的应用[J].农业工程学报,2009,25(5):127.DOI:10.3969/j.issn.1002-6819.2009.05.24.
[17]邹秋霞,杨林楠,彭琳,等.基于Lab空间和K-Means聚类的叶片分割算法研究[J].农机化研究,2015(9):222.DOI:10.3969/j.issn.1003-188X.2015.09.051.
[18]杨帆.数字图像处理及应用(MATLAB版)[M].北京:化学工业出版社,2013.