基于机器视觉的玉米果穗性状参数测量方法研究
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北京市重点实验室2018年度科技创新基地培育与发展专项


Measurement Method of Maize Ear Characters Based on Machine Vision
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    摘要:

    在玉米育种、田间测产和提高玉米产量的过程中,均需要对玉米果穗考种,即需要对玉米果穗的穗长、穗粗、穗行数、行粒数和穗粒数等性状参数进行测量。人工考种不仅花费大量的人力物力,而且在考种过程中普遍存在人工劳动强度大、观测效率低、人为干扰导致测试结果不客观及不准确等问题,在很大程度上限制了考种的速度与精度。针对上述问题,利用所研制的自动考种设备和机器视觉方法,通过USB工业相机获取玉米果穗单面性状彩色图像,利用|B-R|模型、(G+B)/2模型将彩色图像分别进行灰度化,利用改进后的一维最大熵阈值分割方法对灰度图像进行二值化,分别得到果穗轮廓二值图像和果穗特征二值图像;通过轮廓二值图像计算果穗放置后的倾斜角,实现果穗轮廓二值图像和特征二值图像的自动纠偏;通过相机标定,得到单位像素对应的实际值,进而得到穗长及穗粗;通过提取局部籽粒特征二值图像,利用水平黑背景点扫描及对扫描曲线的修正获取穗行宽度,通过穗行数修正模型得到果穗的穗行数;通过提取局部单行籽粒特征二值图像,利用垂直黑背景点扫描及对扫描曲线的修正得到行粒数;根据行粒数和穗行数得到穗粒数。试验结果表明,穗长和穗粗平均测量精度分别为98.05%和97.99%,穗行数测量正确率为95%,行粒数平均测量精度为96.29%,穗粒数平均测量精度为95.67%,和实际值相比,穗粗、穗长、行粒数及穗粒数的测量值差异无显著性。单穗玉米果穗机器视觉平均测量速度为600ms/穗,考种设备测量速度为6s/穗,能够满足自动考种设备的使用需求。

    Abstract:

    In the process of maize breeding, yield tests and the improvement of maize production and the examination, including the measurement of the length, diameters, row numbers, grain numbers per row and grain numbers of ears, is necessary. However, manual examination utilized for a long time not only needs to spend a lot of manpower and resources, but also has many problems, such as high labor intensity, low efficiency of observation and the nonobjective and inaccurate results caused by human interference, greatly limiting the speed and accuracy of the operation. Therefore, an automatic equipment of examination with the machinevision was presented. The colour images of singleface characters of maize ears were obtained by industrial cameras through USB. Then the model |B-R| and (G+B)/2 were respectively applied to gray the colour images. After that, the method of segmentation of onedimension maximum entropy was used to achieve binaryzation, obtaining binary images of contours and features of ears separately. Moreover, these two kinds of images were corrected automatically by the calculation of angles of ear contours of binary images. Based on the calibration of cameras, the unit pixel corresponding to the actual value could be gained and then the length and diameters of ears could be calculated. In addition, the width of rows of ears could be got by the scanning of points of horizontal black background and correction of scanning curves, according to the extraction of local features of binaryzation images. The number of rows of ears could be obtained by the modified model of numbers of rows. Furthermore, by the extraction of binaryzation images of local features of single line of grains, the number of grains of rows could be obtained, based on the scanning of the points of black background and its modified curve. Finally, the total number of grains of ears could be computed by the numbers of rows and that of grains in single row. The experimental results showed that the average accuracy of measurement of ear length and ear diameter were 98.05% and 97.99%; the correct rate of measurement of row numbers was 95%; the average measurement accuracy of the number of grains per row was 96.29%; and the average accuracy of measurement of grain numbers was 95.67%. Furthermore, Ttest was conducted to compare the difference with the standard value, demonstrating that there was no significant difference and the equipment was of reliability. The average speed of measurement of the whole ear was less than 600ms per ear, and the measurement speed of the test system was within 6s per ear, meeting the requirement of the automatic equipment of examination. This research provided the basis of equipment and technology for the modern seed industry, even for the development of agricultural information technology.

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吴刚,吴云帆,陈度,李宝胜,郑永军.基于机器视觉的玉米果穗性状参数测量方法研究[J].农业机械学报,2020,51(s2):357-365. WU Gang, WU Yunfan, CHEN Du, LI Baosheng, ZHENG Yongjun. Measurement Method of Maize Ear Characters Based on Machine Vision[J]. Transactions of the Chinese Society for Agricultural Machinery,2020,51(s2):357-365.

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  • 收稿日期:2020-08-17
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  • 在线发布日期: 2020-12-10
  • 出版日期: 2020-12-10