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An Integrated Skeleton Extraction and Pruning Method for Spatial Recognition of Maize Seedlings in MGV and UAV Remote Images

文献类型： 外文期刊

作者： Zhou, Chengquan 1 ; Yang, Guijun 2 ; Liang, Dong 1 ; Yang, Xiaodong 2 ; Xu, Bo 2 ;

作者机构： 1.Anhui Univ, Sch Elect & Informat Engn, Hefei 230039, Anhui, Peoples R China

2.Minist Agr China, Beijing Res Ctr Informat Technol Agr, Key Lab Quantitat Remote Sensing Agr, Beijing 100001, Peoples R China

3.Natl Engn Res Ctr Informat Technol Agr, Beijing 100001, Peoples R China

4.Minist Agr, Key Lab Agriinformat, Beijing 100001, Peoples R China

关键词： Principal-axis direction; recognition of maize seedlings; skeleton extraction; skeleton-burr removal

期刊名称：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING （ 影响因子：5.6； 五年影响因子：6.086 ）

ISSN： 0196-2892

年卷期： 2018 年 56 卷 8 期

页码：

收录情况： SCI

摘要： Methods to obtain accurate phenotypic data of the seedling stage of maize are receiving ever-increasing research attention because such data are very important for crop growth and for estimating crop yield. To obtain such data, we propose herein an algorithm that uses computer vision to accurately recognize maize seedlings from a digital image. First, the red-green-blue (RGB) images acquired by a manned ground vehicle (MGV) and an unmanned aerial vehicle (UAV) are transformed into grayscale image to clarify the details of the images, and the Otsu threshold-segmentation method, which is based on threshold optimization, is used to separate the maize seedlings from the soil background. Next, the external pressure method is used to segment the results of skeleton extraction. After removing the skeleton burr by using the skeleton-deburring method based on saliency theory, the principal component analysis is used to determine the direction of the principal axis of the maize-seedling skeleton. The principal axis is then introduced as a reference to identify the direction angle of the principle axis in binary images. This process allows us to obtain from RGB images the multiple plant parameters that characterize the maize seedling stage. To verify these statistical computer-generated results, we compare them with field measurements of the maize plots. The result of applying the proposed algorithm to the MGV and UAV data sets correlate strongly (R = 0.77-0.86) with the manually collected data. An average of 0.014 s is required to calculate the number of maize seedlings in a plot from two images showing different vegetation coverage, which shows that the proposed algorithm is computationally efficient. These results indicate that the proposed method provides accurate phenotypic data on maize seedlings.

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