干旱区生态环境知识资源中心(Arid): Green-gradient based canopy segmentation: A multipurpose image mining model with potential use in crop phenotyping and canopy studies

Arid

DOI	10.1016/j.compag.2020.105740
	Green-gradient based canopy segmentation: A multipurpose image mining model with potential use in crop phenotyping and canopy studies
	Haghshenas, Abbas; Emam, Yahya
通讯作者	Emam, Y (corresponding author), Shiraz Univ, Dept Plant Prod & Genet, Shiraz, Iran.
来源期刊	COMPUTERS AND ELECTRONICS IN AGRICULTURE
ISSN	0168-1699
EISSN	1872-7107
出版年	2020
卷号	178
英文摘要	Efficient quantification of the sophisticated shading patterns inside the 3D vegetation canopies may improve our understanding of canopy functions and status, which is possible now more than ever, thanks to the high-throughput phenotyping (HTP) platforms. In order to evaluate the option of quantitative characterization of shading patterns, a simple image mining technique named Green-gradient based canopy Segmentation Model (GSM) was developed based on the relative variations in the level of RGB triplets under different illuminations. For this purpose, an archive of ground-based nadir images of heterogeneous wheat canopies (cultivar mixtures) was analyzed. The images were taken from experimental plots of a two-year field experiment conducted during 2014-15 and 2015-16 growing seasons in the semi-arid region of southern Iran. In GSM, the vegetation pixels were categorized into the maximum possible number of 255 groups based on their green levels. Subsequently, the mean red and the mean blue levels of each group were calculated and plotted against the green levels. It is evidenced that the yielded graph could be readily used for (i) identifying and characterizing canopies even as simple as one or two equation(s); (ii) classification of canopy pixels in accordance with the degree of exposure to sunlight; and (iii) accurate prediction of various quantitative properties of canopy including canopy coverage (CC), Normalized difference vegetation index (NDVI), canopy temperature, and also precise classification of experimental plots based on the qualitative characteristics such as subjection to water and cold stresses, date of imaging, and time of irrigation. The introduced model may provide a multipurpose HTP platform and open new windows to canopy studies.
英文关键词	Canopy coverage Canopy temperature Cultivar mixture NDVI Shading pattern
类型	Article
语种	英语
开放获取类型	Green Submitted
收录类别	SCI-E
WOS记录号	WOS:000596392400005
WOS关键词	WHEAT CULTIVAR MIXTURES ; LEAF PHOTOSYNTHESIS ; DIGITAL CAMERAS ; METAANALYSIS ; VEGETATION ; GROWTH ; LIGHT ; DEEP
WOS类目	Agriculture, Multidisciplinary ; Computer Science, Interdisciplinary Applications
WOS研究方向	Agriculture ; Computer Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/348791
作者单位	[Haghshenas, Abbas; Emam, Yahya] Shiraz Univ, Dept Plant Prod & Genet, Shiraz, Iran
推荐引用方式 GB/T 7714	Haghshenas, Abbas,Emam, Yahya. Green-gradient based canopy segmentation: A multipurpose image mining model with potential use in crop phenotyping and canopy studies[J],2020,178.
APA	Haghshenas, Abbas,&Emam, Yahya.(2020).Green-gradient based canopy segmentation: A multipurpose image mining model with potential use in crop phenotyping and canopy studies.COMPUTERS AND ELECTRONICS IN AGRICULTURE,178.
MLA	Haghshenas, Abbas,et al."Green-gradient based canopy segmentation: A multipurpose image mining model with potential use in crop phenotyping and canopy studies".COMPUTERS AND ELECTRONICS IN AGRICULTURE 178(2020).