| 基于无人机遥感影像的育种玉米垄数统计监测 |
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| 引用本文： | 苏伟,蒋坤萍,闫安,刘哲,张明政,王伟.基于无人机遥感影像的育种玉米垄数统计监测[J].农业工程学报,2018,34(10):92-98. |
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| 作者姓名： | 苏伟 蒋坤萍 闫安 刘哲 张明政 王伟 |
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| 作者单位： | 中国农业大学土地科学与技术学院;山东省滕州市田岗学校 |
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| 基金项目： | 十三五国家重点研发计划项目（2017YFD0300903）；国家自然科学基金项目（41671433） |
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| 摘 要： | 为准确、快速的获取区域范围内的育种玉米垄数信息,该研究充分利用无人机(unmanned aerial vehicle,UAV)超低空遥感监测技术,通过提取UAV影像的超绿特征和Hough变换方法提取育种玉米的垄数。研究区为金色农华种业科技股份有限公司崖城育种基地,基地内存在正处于苗期、拔节期和成熟期的玉米试验地块,使用的数据源为利用固定翼瑞士e Bee Ag精细农业用无人机获取的超低空可见光影像。研究过程中,首先计算UAV影像的超绿特征,并进行二值优化与形态学开启运算处理,以分离玉米植株与土壤背景信息,采用3种尺寸的窗口搜索并检测用于垄数提取的定位点;然后,用影像分割投影法提取玉米垄线的中心点,减小后续处理的计算量;最后,对已经提取的直线特征不明显的无人机影像中垄线中心点进行Hough变换,以提取玉米垄数。精度评价结果为:采用3种搜索窗口,苗期地块内的43垄玉米的提取精度分别为97.67%、95.35%、88.37%;拔节期地块内的74垄玉米的提取精度分别为100.00%、100.00%、58.11%;成熟期地块内的44垄玉米的提取精度分别为95.45%、90.91%、88.64%。该研究所提出的基于影像分割投影法和Hough变换可以正确提取不同生育期的玉米垄数,其中以拔节期的玉米垄数提取精度最高,此时的玉米植株在UAV影像上可以识别且又尚未封垄,是提取种植垄数的最佳时相;对于定位点检测,与玉米种植的垄间间隔相近的窗口尺寸(1?15或者1?25)是垄数监测的最佳尺寸。
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| 关 键 词： | 遥感 无人机 监测 育种玉米 垄数 超绿特征 分割投影法 Hough变换 |
| 收稿时间： | 2018/1/2 0:00:00 |
| 修稿时间： | 2018/3/23 0:00:00 |
Monitoring of planted lines for breeding corn using UAV remote sensing image |
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| Su Wei,Jiang Kunping,Yan An,Liu Zhe,Zhang Mingzheng and Wang Wei.Monitoring of planted lines for breeding corn using UAV remote sensing image[J].Transactions of the Chinese Society of Agricultural Engineering,2018,34(10):92-98. |
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| Authors: | Su Wei Jiang Kunping Yan An Liu Zhe Zhang Mingzheng and Wang Wei |
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| Institution: | 1. College of Land Science and Technology, China Agricultural University, Beijing 100083, China;,1. College of Land Science and Technology, China Agricultural University, Beijing 100083, China;,2. Tiangang School of Tengzhou City, Tengzhou 277519, China,1. College of Land Science and Technology, China Agricultural University, Beijing 100083, China;,1. College of Land Science and Technology, China Agricultural University, Beijing 100083, China; and 1. College of Land Science and Technology, China Agricultural University, Beijing 100083, China; |
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| Abstract: | Abstract: The planted line is one of the important phenotypic parameters for breeding corn. And extracting the planted lines of breeding corn quickly and nondestructively is vital to corn breeding trials and research. Unmanned aerial vehicle (UAV) remote sensing technique has advantage in extracting phenotypic parameters of corn canopy, which can be used to extract the phenotyping parameters in large area for breeding corn. This research is aiming at extracting the planted lines for breeding corn using UAV image acquired from DJS1000+ UAV platform by calclulating the super-green feature and Hough transform of the UAV images of 3 different growing seasons, i. e. seeding stage, jointing stage, and maturation period. The first step is calculating the super-green features of 3 UAV images, and then binary optimization and morphological open operation are performed so as to separate corn canopy and soil background. In addition, we use 3 different window sizes, i.e. 1?15, 1?25, 1?50, to search and detect the locating middle points for extracting planted line of breeding corn in 3 different growing seasons. Next step, the center point of corn ridge line is identified by projecting the segmented objects. The judging conditions of left border and right border for center points extraction are as follows: The points will be classified as left border points if the pixel mean is greater than pixel of column j-1 and the pixel mean is less than pixel of column j+1; and the points will be classified as right border points if value the pixel mean is less than pixel of column j-1 and the pixel mean is greater than pixel of column j+1; the central location will be labeled as the central point of planted line. Finally, the line of breeding corn is identified by Hough transform of the above center points of corn ridge line. Hough transform is casting all the points in image domain to the points in transforming domain, and one point in image domain is corresponding to one sine curve in transforming domain. Therefore, the sine curves that are corresponding to the points located on the same line will be intersected at one same point, which will establish the line in image domain. We select 3 breeding corn plots in 3 different growing periods i.e. seeding stage, jointing stage, and maturation period. The results indicate that the planted line of breeding corn in jointing stage can be extracted more accurately than that in seeding stage and maturation period. There were 74 lines used in planted line extraction experiment in jointing stage, and our extraction results are 74, 74, and 105 lines using 3 different window sizes respectively. In this growing stage, the corn plants are big enough for planted line extraction and the leaves between different lines do not connect meanwhile. There were 43 lines used in planted line extraction experiment in seeding stage, and our extraction results are 42, 45, and 58 lines using 3 different window sizes respectively. The leaves between different lines have already connected, which affects the planted line extraction. And there were 44 lines used in planted line extraction experiment in maturation stage, and our extraction results are 46, 40, and 49 lines using 3 different window sizes respectively. In this seeding stage, the leaves are too small (1-2 cm) and the planted line extraction is more difficult. So our method can be used to extract the number of breeding corn lines, and the optimal growing stage for planted line extraction is jointing stage and the window size for searching and detecting the locating middle points should be similar to the row spacing of planted corn. |
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| Keywords: | remote sensing unmanned aerial vehicle monitoring breeding corn planted lines super-green feature projection based on segmented objects Hough transform |
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