Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping.

Cosegmentation is a newly emerging computer vision technique used to segment an object from the background by processing multiple images at the same time. Traditional plant phenotyping analysis uses thresholding segmentation methods which result in high segmentation accuracy. Although there are prop...

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Autores principales:	Rubi Quiñones, Francisco Munoz-Arriola, Sruti Das Choudhury, Ashok Samal
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e91b1684bf354b1ba2cdd814786a511b
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spelling	oai:doaj.org-article:e91b1684bf354b1ba2cdd814786a511b2021-12-02T20:08:33ZMulti-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping.1932-620310.1371/journal.pone.0257001https://doaj.org/article/e91b1684bf354b1ba2cdd814786a511b2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0257001https://doaj.org/toc/1932-6203Cosegmentation is a newly emerging computer vision technique used to segment an object from the background by processing multiple images at the same time. Traditional plant phenotyping analysis uses thresholding segmentation methods which result in high segmentation accuracy. Although there are proposed machine learning and deep learning algorithms for plant segmentation, predictions rely on the specific features being present in the training set. The need for a multi-featured dataset and analytics for cosegmentation becomes critical to better understand and predict plants' responses to the environment. High-throughput phenotyping produces an abundance of data that can be leveraged to improve segmentation accuracy and plant phenotyping. This paper introduces four datasets consisting of two plant species, Buckwheat and Sunflower, each split into control and drought conditions. Each dataset has three modalities (Fluorescence, Infrared, and Visible) with 7 to 14 temporal images that are collected in a high-throughput facility at the University of Nebraska-Lincoln. The four datasets (which will be collected under the CosegPP data repository in this paper) are evaluated using three cosegmentation algorithms: Markov random fields-based, Clustering-based, and Deep learning-based cosegmentation, and one commonly used segmentation approach in plant phenotyping. The integration of CosegPP with advanced cosegmentation methods will be the latest benchmark in comparing segmentation accuracy and finding areas of improvement for cosegmentation methodology.Rubi QuiñonesFrancisco Munoz-ArriolaSruti Das ChoudhuryAshok SamalPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0257001 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Rubi Quiñones Francisco Munoz-Arriola Sruti Das Choudhury Ashok Samal Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping.
description	Cosegmentation is a newly emerging computer vision technique used to segment an object from the background by processing multiple images at the same time. Traditional plant phenotyping analysis uses thresholding segmentation methods which result in high segmentation accuracy. Although there are proposed machine learning and deep learning algorithms for plant segmentation, predictions rely on the specific features being present in the training set. The need for a multi-featured dataset and analytics for cosegmentation becomes critical to better understand and predict plants' responses to the environment. High-throughput phenotyping produces an abundance of data that can be leveraged to improve segmentation accuracy and plant phenotyping. This paper introduces four datasets consisting of two plant species, Buckwheat and Sunflower, each split into control and drought conditions. Each dataset has three modalities (Fluorescence, Infrared, and Visible) with 7 to 14 temporal images that are collected in a high-throughput facility at the University of Nebraska-Lincoln. The four datasets (which will be collected under the CosegPP data repository in this paper) are evaluated using three cosegmentation algorithms: Markov random fields-based, Clustering-based, and Deep learning-based cosegmentation, and one commonly used segmentation approach in plant phenotyping. The integration of CosegPP with advanced cosegmentation methods will be the latest benchmark in comparing segmentation accuracy and finding areas of improvement for cosegmentation methodology.
format	article
author	Rubi Quiñones Francisco Munoz-Arriola Sruti Das Choudhury Ashok Samal
author_facet	Rubi Quiñones Francisco Munoz-Arriola Sruti Das Choudhury Ashok Samal
author_sort	Rubi Quiñones
title	Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping.
title_short	Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping.
title_full	Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping.
title_fullStr	Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping.
title_full_unstemmed	Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping.
title_sort	multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/e91b1684bf354b1ba2cdd814786a511b
work_keys_str_mv	AT rubiquinones multifeaturedatarepositorydevelopmentandanalyticsforimagecosegmentationinhighthroughputplantphenotyping AT franciscomunozarriola multifeaturedatarepositorydevelopmentandanalyticsforimagecosegmentationinhighthroughputplantphenotyping AT srutidaschoudhury multifeaturedatarepositorydevelopmentandanalyticsforimagecosegmentationinhighthroughputplantphenotyping AT ashoksamal multifeaturedatarepositorydevelopmentandanalyticsforimagecosegmentationinhighthroughputplantphenotyping
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Multi-feature data repository development and analytics for image cosegmentation in high-throughput plant phenotyping.

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