| 基于穗粒分布图的玉米果穗表型性状参数计算方法 |
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| 引用本文： | 杜建军,郭新宇,王传宇,肖伯祥.基于穗粒分布图的玉米果穗表型性状参数计算方法[J].农业工程学报,2016,32(13):168-176. |
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| 作者姓名： | 杜建军 郭新宇 王传宇 肖伯祥 |
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| 作者单位： | 1. 北京市农林科学院,北京农业信息技术研究中心,北京100097;2. 国家农业信息化工程技术研究中心,北京,100097;3. 数字植物北京重点实验室,北京,100097 |
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| 基金项目： | 农业部行业科技计划项目(201203026)；国家自然科学基金课题(31171454)；北京市农林科学院科技创新团队(JNKYT201604) |
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| 摘 要： | 玉米果穗表型性状是玉米育种、产量预测的重要参数,提出一种基于穗粒分布图的玉米果穗性状计算方法,全面解析玉米果穗和穗粒的几何、数量和颜色等表型性状。该文利用步进电机驱动果穗转动来获取果穗主要侧面图像,采用果穗畸变校正方法生成标准果穗图像序列,在像素尺度进行果穗轮廓分析,建立图像序列中果穗轮廓映射关系并生成果穗三维模型,在穗粒尺度拼接果穗整个表面的穗粒分布图,计算出果穗和穗粒的各项表型性状。试验结果表明,提出的表型性状计算方法对穗型及穗粒分布规则的玉米果穗具有较高检测精度,其中穗行数、行粒数、总粒数、果穗长和果穗粗的平均计算精度分别为98.231%、94.351%、96.921%、98.956%和98.165%。
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| 关 键 词： | 图像分割 三维 畸变 玉米果穗 表型性状 校正 |
| 收稿时间： | 2015/10/15 0:00:00 |
| 修稿时间： | 2016/4/16 0:00:00 |
Computation method of phenotypic parameters based on distribution map of kernels for corn ears |
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| Du Jianjun,Guo Xinyu,Wang Chuanyu and Xiao Boxiang.Computation method of phenotypic parameters based on distribution map of kernels for corn ears[J].Transactions of the Chinese Society of Agricultural Engineering,2016,32(13):168-176. |
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| Authors: | Du Jianjun Guo Xinyu Wang Chuanyu and Xiao Boxiang |
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| Institution: | 1.Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China;2.National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China;3.Beijing Key Lab of Digital Plant, Beijing 100097, China,1.Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China;2.National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China;3.Beijing Key Lab of Digital Plant, Beijing 100097, China,1.Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China;2.National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China;3.Beijing Key Lab of Digital Plant, Beijing 100097, China and 1.Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China;2.National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China;3.Beijing Key Lab of Digital Plant, Beijing 100097, China |
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| Abstract: | Phenotypic traits of corn ears are important parameters for maize breeding and production forecast.A phenotypic computation method based on distribution map of kernels for corn ears is presented to comprehensively analyze the geometrical, quantitative, color and texture traits of corn ear and its kernels.A phenotypic detection system of corn ears, which consists of stepping motor, charge coupled device (CCD) camera, lightemitting diode (LED) back lights, image acquisition card and semiclosed box, is designed to capture main side images of corn ear.Corn ear is fixed vertically on a turn table driven by stepping motor, and thus image sequence of corn ear can be captured from designated angles and covers the entire surface of corn ear.In this study, 4 orthotropic images are captured to build threedimensional reference frame of corn ear.Firstly, axial and radial distortion corrections are successively applied to image sequences and generate standard image sequence of corn ear.Therein, axial distortion correction regularizes the heights of corn ear for image sequence to their average height, and radial distortion correction is used to recover size and shape of kernels on the surface of corn ear, since those kernels lie on border regions of corn ears which have obvious distortion.A dedicated image segmentation method, which has utilized geometrical and color traits of kernels of corn ear to the greatest extent, is then used to extract all kernels of corn ear from corrected image sequence.Meanwhile, contour lines and split lines of corn ear in image sequence are calculated based on pixel scale, and used to generate the mapping relationship among the images of corn ear.Contour lines of corn ear can be used to generate a threedimensional surface model by transforming the coordinates of lines from twodimensional to threedimensional space, and then the threedimensional model of corn ear is used to calculate geometrical traits, e.g.perimeter, surface and volume.Moreover, contour lines of corn ear can also output corresponding split lines which split each corn image into different regions, and further classify kernels into different types according to position relationship between kernels and split lines.On the basis of kernel scale, the classified kernels from segmented image sequence can be assembled together to generate a distribution map which describes entire surface kernels of corn ear.According to the distribution map of kernels and segmented image sequence, the quantitative and geometrical traits of kernels, such as rows per ear, kernels per row, total kernels and kernel thickness can be accurately calculated using Delaunay and BellmanFord methods.The proposed method and system can simultaneously detect multiple types of phenotypic traits from image sequence of corn ear, and have higher accuracy in almost all phenotypic traits than the detection method based on single profile image of corn ear.Experimental results demonstrate that the computed traits have good consistence with the observed values, and the average computation accuracy of main traits, i.e.rows per ear, kernels per row, total kernels, ear length and ear diameter, can respectively reach 98.231%, 94.351%, 96.921%, 98.956% and 98.165%.Thus, the proposed method can be applied for precise phenotypic detection and breeding of corn ears. |
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| Keywords: | image segmentation three dimensional distortion corn ear phenotypic traits correction |
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