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Automatic estimation of heading date of paddy rice using deep learning

BACKGROUND: Accurate estimation of heading date of paddy rice greatly helps the breeders to understand the adaptability of different crop varieties in a given location. The heading date also plays a vital role in determining grain yield for research experiments. Visual examination of the crop is lab...

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Autores principales: Desai, Sai Vikas, Balasubramanian, Vineeth N., Fukatsu, Tokihiro, Ninomiya, Seishi, Guo, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6626381/
https://www.ncbi.nlm.nih.gov/pubmed/31338116
http://dx.doi.org/10.1186/s13007-019-0457-1
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author Desai, Sai Vikas
Balasubramanian, Vineeth N.
Fukatsu, Tokihiro
Ninomiya, Seishi
Guo, Wei
author_facet Desai, Sai Vikas
Balasubramanian, Vineeth N.
Fukatsu, Tokihiro
Ninomiya, Seishi
Guo, Wei
author_sort Desai, Sai Vikas
collection PubMed
description BACKGROUND: Accurate estimation of heading date of paddy rice greatly helps the breeders to understand the adaptability of different crop varieties in a given location. The heading date also plays a vital role in determining grain yield for research experiments. Visual examination of the crop is laborious and time consuming. Therefore, quick and precise estimation of heading date of paddy rice is highly essential. RESULTS: In this work, we propose a simple pipeline to detect regions containing flowering panicles from ground level RGB images of paddy rice. Given a fixed region size for an image, the number of regions containing flowering panicles is directly proportional to the number of flowering panicles present. Consequently, we use the flowering panicle region counts to estimate the heading date of the crop. The method is based on image classification using Convolutional Neural Networks. We evaluated the performance of our algorithm on five time series image sequences of three different varieties of rice crops. When compared to the previous work on this dataset, the accuracy and general versatility of the method has been improved and heading date has been estimated with a mean absolute error of less than 1 day. CONCLUSION: An efficient heading date estimation method has been described for rice crops using time series RGB images of crop under natural field conditions. This study demonstrated that our method can reliably be used as a replacement of manual observation to detect the heading date of rice crops. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13007-019-0457-1) contains supplementary material, which is available to authorized users.
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spelling pubmed-66263812019-07-23 Automatic estimation of heading date of paddy rice using deep learning Desai, Sai Vikas Balasubramanian, Vineeth N. Fukatsu, Tokihiro Ninomiya, Seishi Guo, Wei Plant Methods Methodology BACKGROUND: Accurate estimation of heading date of paddy rice greatly helps the breeders to understand the adaptability of different crop varieties in a given location. The heading date also plays a vital role in determining grain yield for research experiments. Visual examination of the crop is laborious and time consuming. Therefore, quick and precise estimation of heading date of paddy rice is highly essential. RESULTS: In this work, we propose a simple pipeline to detect regions containing flowering panicles from ground level RGB images of paddy rice. Given a fixed region size for an image, the number of regions containing flowering panicles is directly proportional to the number of flowering panicles present. Consequently, we use the flowering panicle region counts to estimate the heading date of the crop. The method is based on image classification using Convolutional Neural Networks. We evaluated the performance of our algorithm on five time series image sequences of three different varieties of rice crops. When compared to the previous work on this dataset, the accuracy and general versatility of the method has been improved and heading date has been estimated with a mean absolute error of less than 1 day. CONCLUSION: An efficient heading date estimation method has been described for rice crops using time series RGB images of crop under natural field conditions. This study demonstrated that our method can reliably be used as a replacement of manual observation to detect the heading date of rice crops. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13007-019-0457-1) contains supplementary material, which is available to authorized users. BioMed Central 2019-07-13 /pmc/articles/PMC6626381/ /pubmed/31338116 http://dx.doi.org/10.1186/s13007-019-0457-1 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 Methodology
Desai, Sai Vikas
Balasubramanian, Vineeth N.
Fukatsu, Tokihiro
Ninomiya, Seishi
Guo, Wei
Automatic estimation of heading date of paddy rice using deep learning
title Automatic estimation of heading date of paddy rice using deep learning
title_full Automatic estimation of heading date of paddy rice using deep learning
title_fullStr Automatic estimation of heading date of paddy rice using deep learning
title_full_unstemmed Automatic estimation of heading date of paddy rice using deep learning
title_short Automatic estimation of heading date of paddy rice using deep learning
title_sort automatic estimation of heading date of paddy rice using deep learning
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6626381/
https://www.ncbi.nlm.nih.gov/pubmed/31338116
http://dx.doi.org/10.1186/s13007-019-0457-1
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