A method for estimating spikelet number per panicle: Integrating image analysis and a 5-point calibration model

Spikelet number per panicle (SNPP) is one of the most important yield components used to estimate rice yields. The use of high-throughput quantitative image analysis methods for understanding the diversity of the panicle has increased rapidly. However, it is difficult to simultaneously extract panic...

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Main Authors:	Zhao, Sanqin, Gu, Jiabing, Zhao, Youyong, Hassan, Muhammad, Li, Yinian, Ding, Weimin
Format:	Online
Language:	English
Published:	Nature Publishing Group 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4635573/

id	pubmed-4635573
recordtype	oai_dc
spelling	pubmed-46355732015-11-25 A method for estimating spikelet number per panicle: Integrating image analysis and a 5-point calibration model Zhao, Sanqin Gu, Jiabing Zhao, Youyong Hassan, Muhammad Li, Yinian Ding, Weimin Article Spikelet number per panicle (SNPP) is one of the most important yield components used to estimate rice yields. The use of high-throughput quantitative image analysis methods for understanding the diversity of the panicle has increased rapidly. However, it is difficult to simultaneously extract panicle branch and spikelet/grain information from images at the same resolution due to the different scales of these traits. To use a lower resolution and meet the accuracy requirement, we proposed an interdisciplinary method that integrated image analysis and a 5-point calibration model to rapidly estimate SNPP. First, a linear relationship model between the total length of the primary branch (TLPB) and the SNPP was established based on the physiological characteristics of the panicle. Second, the TLPB and area (the primary branch region) traits were rapidly extracted by developing image analysis algorithm. Finally, a 5-point calibration method was adopted to improve the universality of the model. The number of panicle samples that the error of the SNPP estimates was less than 10% was greater than 90% by the proposed method. The estimation accuracy was consistent with the accuracy determined using manual measurements. The proposed method uses available concepts and techniques for automated estimations of rice yield information. Nature Publishing Group 2015-11-06 /pmc/articles/PMC4635573/ /pubmed/26542412 http://dx.doi.org/10.1038/srep16241 Text en Copyright © 2015, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Zhao, Sanqin Gu, Jiabing Zhao, Youyong Hassan, Muhammad Li, Yinian Ding, Weimin
spellingShingle	Zhao, Sanqin Gu, Jiabing Zhao, Youyong Hassan, Muhammad Li, Yinian Ding, Weimin A method for estimating spikelet number per panicle: Integrating image analysis and a 5-point calibration model
author_facet	Zhao, Sanqin Gu, Jiabing Zhao, Youyong Hassan, Muhammad Li, Yinian Ding, Weimin
author_sort	Zhao, Sanqin
title	A method for estimating spikelet number per panicle: Integrating image analysis and a 5-point calibration model
title_short	A method for estimating spikelet number per panicle: Integrating image analysis and a 5-point calibration model
title_full	A method for estimating spikelet number per panicle: Integrating image analysis and a 5-point calibration model
title_fullStr	A method for estimating spikelet number per panicle: Integrating image analysis and a 5-point calibration model
title_full_unstemmed	A method for estimating spikelet number per panicle: Integrating image analysis and a 5-point calibration model
title_sort	method for estimating spikelet number per panicle: integrating image analysis and a 5-point calibration model
description	Spikelet number per panicle (SNPP) is one of the most important yield components used to estimate rice yields. The use of high-throughput quantitative image analysis methods for understanding the diversity of the panicle has increased rapidly. However, it is difficult to simultaneously extract panicle branch and spikelet/grain information from images at the same resolution due to the different scales of these traits. To use a lower resolution and meet the accuracy requirement, we proposed an interdisciplinary method that integrated image analysis and a 5-point calibration model to rapidly estimate SNPP. First, a linear relationship model between the total length of the primary branch (TLPB) and the SNPP was established based on the physiological characteristics of the panicle. Second, the TLPB and area (the primary branch region) traits were rapidly extracted by developing image analysis algorithm. Finally, a 5-point calibration method was adopted to improve the universality of the model. The number of panicle samples that the error of the SNPP estimates was less than 10% was greater than 90% by the proposed method. The estimation accuracy was consistent with the accuracy determined using manual measurements. The proposed method uses available concepts and techniques for automated estimations of rice yield information.
publisher	Nature Publishing Group
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4635573/
_version_	1613498313557934080

A method for estimating spikelet number per panicle: Integrating image analysis and a 5-point calibration model

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