IB-CAS  > 植被与环境变化国家重点实验室
Leaf to panicle ratio (LPR): a new physiological trait indicative of source and sink relation in japonica rice based on deep learning
Yang, Zongfeng; Gao, Shang1; Xiao, Feng; Li, Ganghua; Ding, Yangfeng; Guo, Qinghua1; Paul, Matthew J.; Liu, Zhenghui3
2020
Source PublicationPLANT METHODS
Volume16Issue:1
AbstractBackground Identification and characterization of new traits with sound physiological foundation is essential for crop breeding and production management. Deep learning has been widely used in image data analysis to explore spatial and temporal information on crop growth and development, thus strengthening the power of identification of physiological traits. Taking the advantage of deep learning, this study aims to develop a novel trait of canopy structure that integrate source and sink in japonica rice. Results We applied a deep learning approach to accurately segment leaf and panicle, and subsequently developed the procedure of GvCrop to calculate the leaf to panicle ratio (LPR) of rice canopy during grain filling stage. Images of training dataset were captured in the field experiments, with large variations in camera shooting angle, the elevation and the azimuth angles of the sun, rice genotype, and plant phenological stages. Accurately labeled by manually annotating the panicle and leaf regions, the resulting dataset were used to train FPN-Mask (Feature Pyramid Network Mask) models, consisting of a backbone network and a task-specific sub-network. The model with the highest accuracy was then selected to check the variations in LPR among 192 rice germplasms and among agronomical practices. Despite the challenging field conditions, FPN-Mask models achieved a high detection accuracy, with Pixel Accuracy being 0.99 for panicles and 0.98 for leaves. The calculated LPR displayed large spatial and temporal variations as well as genotypic differences. In addition, it was responsive to agronomical practices such as nitrogen fertilization and spraying of plant growth regulators. Conclusion Deep learning technique can achieve high accuracy in simultaneous detection of panicle and leaf data from complex rice field images. The proposed FPN-Mask model is applicable to detect and quantify crop performance under field conditions. The newly identified trait of LPR should provide a high throughput protocol for breeders to select superior rice cultivars as well as for agronomists to precisely manage field crops that have a good balance of source and sink.
KeywordPlant phenotyping Leaf and panicle detection Deep learning Physiological trait Leaf to panicle ratio (LPR) Japonica rice
Subject AreaBiochemical Research Methods ; Plant Sciences
DOI10.1186/s13007-020-00660-y
Indexed BySCI
Language英语
WOS KeywordHIGH-THROUGHPUT ; SEGMENTATION ; PHENOMICS ; YIELD ; WHEAT ; AREA ; EAR
WOS Research AreaBiochemistry & Molecular Biology ; Plant Sciences
WOS IDWOS:000566943100003
PublisherBMC
SubtypeArticle
Publication PlaceLONDON
EISSN1746-4811
Funding OrganizationNational Key R&D Program, Ministry of Science and Technology, China [2017YFD0300103] ; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [31771719] ; National High Technology Research and Development Program of ChinaNational High Technology Research and Development Program of China [2014AA10A605] ; Biological and Biotechnological Sciences Research Council of the United Kingdom ; Designing Future Wheat Strategic Programme [BB/P016855/1]
Corresponding Author Emailqguo@ibcas.ac.cn ; liuzh@njau.edu.cn
OAGreen Published, gold, Green Submitted
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ibcas.ac.cn/handle/2S10CLM1/21593
Collection植被与环境变化国家重点实验室
Affiliation1.Nanjing Agr Univ, Coll Agr, Nanjing 210095, Peoples R China
2.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China
3.Paul, Matthew J.] Rothamsted Res, Plant Sci, Harpenden AL5 2JQ, Herts, England
4.Nanjing Agr Univ, Collaborat Innovat Ctr Modern Crop Prod, Nanjing 210095, Peoples R China
Recommended Citation
GB/T 7714
Yang, Zongfeng,Gao, Shang,Xiao, Feng,et al. Leaf to panicle ratio (LPR): a new physiological trait indicative of source and sink relation in japonica rice based on deep learning[J]. PLANT METHODS,2020,16(1).
APA Yang, Zongfeng.,Gao, Shang.,Xiao, Feng.,Li, Ganghua.,Ding, Yangfeng.,...&Liu, Zhenghui.(2020).Leaf to panicle ratio (LPR): a new physiological trait indicative of source and sink relation in japonica rice based on deep learning.PLANT METHODS,16(1).
MLA Yang, Zongfeng,et al."Leaf to panicle ratio (LPR): a new physiological trait indicative of source and sink relation in japonica rice based on deep learning".PLANT METHODS 16.1(2020).
Files in This Item:
File Name/Size DocType Version Access License
Yang-2020-Leaf to pa(4429KB)期刊论文出版稿开放获取CC BY-NC-SAView Application Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Yang, Zongfeng]'s Articles
[Gao, Shang]'s Articles
[Xiao, Feng]'s Articles
Baidu academic
Similar articles in Baidu academic
[Yang, Zongfeng]'s Articles
[Gao, Shang]'s Articles
[Xiao, Feng]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Yang, Zongfeng]'s Articles
[Gao, Shang]'s Articles
[Xiao, Feng]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Yang-2020-Leaf to panicle ratio (LPR)_ a new p.pdf
Format: Adobe PDF
This file does not support browsing at this time
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.