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Image analysis-based recognition and quantification of grain number per panicle in rice

BACKGROUND: The number grain per panicle of rice is an important phenotypic trait and a significant index for variety screening and cultivation management. The methods that are currently used to count the number of grains per panicle are manually conducted, making them labor intensive and time consu...

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Autores principales: Wu, Wei, Liu, Tao, Zhou, Ping, Yang, Tianle, Li, Chunyan, Zhong, Xiaochun, Sun, Chengming, Liu, Shengping, Guo, Wenshan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6822408/
https://www.ncbi.nlm.nih.gov/pubmed/31695727
http://dx.doi.org/10.1186/s13007-019-0510-0
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author Wu, Wei
Liu, Tao
Zhou, Ping
Yang, Tianle
Li, Chunyan
Zhong, Xiaochun
Sun, Chengming
Liu, Shengping
Guo, Wenshan
author_facet Wu, Wei
Liu, Tao
Zhou, Ping
Yang, Tianle
Li, Chunyan
Zhong, Xiaochun
Sun, Chengming
Liu, Shengping
Guo, Wenshan
author_sort Wu, Wei
collection PubMed
description BACKGROUND: The number grain per panicle of rice is an important phenotypic trait and a significant index for variety screening and cultivation management. The methods that are currently used to count the number of grains per panicle are manually conducted, making them labor intensive and time consuming. Existing image-based grain counting methods had difficulty in separating overlapped grains. RESULTS: In this study, we aimed to develop an image analysis-based method to quickly quantify the number of rice grains per panicle. We compared the counting accuracy of several methods among different image acquisition devices and multiple panicle shapes on both Indica and Japonica subspecies of rice. The linear regression model developed in this study had a grain counting accuracy greater than 96% and 97% for Japonica and Indica rice, respectively. Moreover, while the deep learning model that we used was more time consuming than the linear regression model, the average counting accuracy was greater than 99%. CONCLUSIONS: We developed a rice grain counting method that accurately counts the number of grains on a detached panicle, and believe this method can be a huge asset for guiding the development of high throughput methods for counting the grain number per panicle in other crops.
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spelling pubmed-68224082019-11-06 Image analysis-based recognition and quantification of grain number per panicle in rice Wu, Wei Liu, Tao Zhou, Ping Yang, Tianle Li, Chunyan Zhong, Xiaochun Sun, Chengming Liu, Shengping Guo, Wenshan Plant Methods Research BACKGROUND: The number grain per panicle of rice is an important phenotypic trait and a significant index for variety screening and cultivation management. The methods that are currently used to count the number of grains per panicle are manually conducted, making them labor intensive and time consuming. Existing image-based grain counting methods had difficulty in separating overlapped grains. RESULTS: In this study, we aimed to develop an image analysis-based method to quickly quantify the number of rice grains per panicle. We compared the counting accuracy of several methods among different image acquisition devices and multiple panicle shapes on both Indica and Japonica subspecies of rice. The linear regression model developed in this study had a grain counting accuracy greater than 96% and 97% for Japonica and Indica rice, respectively. Moreover, while the deep learning model that we used was more time consuming than the linear regression model, the average counting accuracy was greater than 99%. CONCLUSIONS: We developed a rice grain counting method that accurately counts the number of grains on a detached panicle, and believe this method can be a huge asset for guiding the development of high throughput methods for counting the grain number per panicle in other crops. BioMed Central 2019-10-31 /pmc/articles/PMC6822408/ /pubmed/31695727 http://dx.doi.org/10.1186/s13007-019-0510-0 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Wu, Wei
Liu, Tao
Zhou, Ping
Yang, Tianle
Li, Chunyan
Zhong, Xiaochun
Sun, Chengming
Liu, Shengping
Guo, Wenshan
Image analysis-based recognition and quantification of grain number per panicle in rice
title Image analysis-based recognition and quantification of grain number per panicle in rice
title_full Image analysis-based recognition and quantification of grain number per panicle in rice
title_fullStr Image analysis-based recognition and quantification of grain number per panicle in rice
title_full_unstemmed Image analysis-based recognition and quantification of grain number per panicle in rice
title_short Image analysis-based recognition and quantification of grain number per panicle in rice
title_sort image analysis-based recognition and quantification of grain number per panicle in rice
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6822408/
https://www.ncbi.nlm.nih.gov/pubmed/31695727
http://dx.doi.org/10.1186/s13007-019-0510-0
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