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Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep Learning

The use of high-throughput phenotyping with imaging and machine learning to monitor seedling growth is a tough yet intriguing subject in plant research. This has been recently addressed with low-cost RGB imaging sensors and deep learning during day time. RGB-Depth imaging devices are also accessible...

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Detalles Bibliográficos
Autores principales: Garbouge, Hadhami, Rasti, Pejman, Rousseau, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8708901/
https://www.ncbi.nlm.nih.gov/pubmed/34960519
http://dx.doi.org/10.3390/s21248425
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author Garbouge, Hadhami
Rasti, Pejman
Rousseau, David
author_facet Garbouge, Hadhami
Rasti, Pejman
Rousseau, David
author_sort Garbouge, Hadhami
collection PubMed
description The use of high-throughput phenotyping with imaging and machine learning to monitor seedling growth is a tough yet intriguing subject in plant research. This has been recently addressed with low-cost RGB imaging sensors and deep learning during day time. RGB-Depth imaging devices are also accessible at low-cost and this opens opportunities to extend the monitoring of seedling during days and nights. In this article, we investigate the added value to fuse RGB imaging with depth imaging for this task of seedling growth stage monitoring. We propose a deep learning architecture along with RGB-Depth fusion to categorize the three first stages of seedling growth. Results show an average performance improvement of [Formula: see text] correct recognition rate by comparison with the sole use of RGB images during the day. The best performances are obtained with the early fusion of RGB and Depth. Also, Depth is shown to enable the detection of growth stage in the absence of the light.
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spelling pubmed-87089012021-12-25 Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep Learning Garbouge, Hadhami Rasti, Pejman Rousseau, David Sensors (Basel) Article The use of high-throughput phenotyping with imaging and machine learning to monitor seedling growth is a tough yet intriguing subject in plant research. This has been recently addressed with low-cost RGB imaging sensors and deep learning during day time. RGB-Depth imaging devices are also accessible at low-cost and this opens opportunities to extend the monitoring of seedling during days and nights. In this article, we investigate the added value to fuse RGB imaging with depth imaging for this task of seedling growth stage monitoring. We propose a deep learning architecture along with RGB-Depth fusion to categorize the three first stages of seedling growth. Results show an average performance improvement of [Formula: see text] correct recognition rate by comparison with the sole use of RGB images during the day. The best performances are obtained with the early fusion of RGB and Depth. Also, Depth is shown to enable the detection of growth stage in the absence of the light. MDPI 2021-12-17 /pmc/articles/PMC8708901/ /pubmed/34960519 http://dx.doi.org/10.3390/s21248425 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Garbouge, Hadhami
Rasti, Pejman
Rousseau, David
Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep Learning
title Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep Learning
title_full Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep Learning
title_fullStr Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep Learning
title_full_unstemmed Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep Learning
title_short Enhancing the Tracking of Seedling Growth Using RGB-Depth Fusion and Deep Learning
title_sort enhancing the tracking of seedling growth using rgb-depth fusion and deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8708901/
https://www.ncbi.nlm.nih.gov/pubmed/34960519
http://dx.doi.org/10.3390/s21248425
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