摘要
在弱光条件下,采用色调(H)和饱和度(S)颜色分量的K均值聚类分析结合相应色差运算方法,对覆膜玉米冠层图像进行分割,并将分割所得影像的二值图分别与超绿、超红和超绿-超红算法分割结果进行比较。结果表明,该方法更能精确反映玉米的冠层形状。将该方法得到的玉米冠层覆盖度计算结果与Samplepoint软件分析结果进行比较发现,前者均方根误差取值较小,仅为0.004 2,分割误差率低至3.37%,分割图像准确率高。综合分析表明,在弱光背景下,基于H和S颜色分量的K均值聚类分析结合色差运算的分割方法对覆膜玉米冠层的分割结果准确可靠。
Percent ground cover of vegetation is an important parameter which received attention of both agronomists and ecologists. Not only does it reflect dynamic growth of plants in a long time, but also it is associated with abstraction of photosynthesis available radiation（APAR） of plants. So far as the maize crop cover is concerned, current researches mainly focused on calculating percent ground cover of maize on bare ground. It is a fact that plastic film mulching has been widely adopted for maize planting due to its effect on reducing water loss, regulating soil temperature, improving the infiltration of rainwater into the soil, enhancing soil water retention, accelerating crop growth, and significantly increasing crop yield. In addition, the recent advances in image analysis software offered potential for analyzing the digital camera images of habitat to objectively quantify ground cover of vegetation in a repeatable and timely manner too. Here we evaluated use of Matlab software for analyzing the digital photographs of plastic-film maize to quantify the percent ground cover.In this study, the images of plastic-film maize were firstly taken by smart phone under weak light condition, which were JPEG（joint photographic expert group） format here and were in 1 358×1 314 resolution. Then the method combined the K-mean clustering analysis of hue（H） and saturation（S） color components with performing a corresponding mathematical operation was proposed to discriminate the maize and background. The proposed method was comprised of three main steps. First, color images yielding red（R）, green（G）, and blue（B） subimages were mathematically transformed to hue（H）,saturation（S）, and intensity（I） ones. And then, the images were respectively segmented using the methods of excess green（Ex G）, excess red（Ex R）, excess green minus excess red（Ex G-Ex R）, and Otsu thresholding of excess green, excess red and excess green minus excess red. Second, the K-mean clustering analysis of H and
出处
《浙江大学学报（农业与生命科学版）》
CAS
CSCD
北大核心
2017年第5期649-656,共8页
Journal of Zhejiang University:Agriculture and Life Sciences
基金
国家高技术研究发展计划(863计划)(2013AA102904)
关键词
色调
饱和度
K均值聚类
图像分割
玉米冠层
hue
saturation
K-meanclustering analysis
image segmentation
corn canopy
