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PocketMaize: An Android-Smartphone Application for Maize Plant Phenotyping
A low-cost portable wild phenotyping system is useful for breeders to obtain detailed phenotypic characterization to identify promising wild species. However, compared with the larger, faster, and more advanced in-laboratory phenotyping systems developed in recent years, the progress for smaller phe...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8656718/ https://www.ncbi.nlm.nih.gov/pubmed/34899792 http://dx.doi.org/10.3389/fpls.2021.770217 |
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author | Liu, Lingbo Yu, Lejun Wu, Dan Ye, Junli Feng, Hui Liu, Qian Yang, Wanneng |
author_facet | Liu, Lingbo Yu, Lejun Wu, Dan Ye, Junli Feng, Hui Liu, Qian Yang, Wanneng |
author_sort | Liu, Lingbo |
collection | PubMed |
description | A low-cost portable wild phenotyping system is useful for breeders to obtain detailed phenotypic characterization to identify promising wild species. However, compared with the larger, faster, and more advanced in-laboratory phenotyping systems developed in recent years, the progress for smaller phenotyping systems, which provide fast deployment and potential for wide usage in rural and wild areas, is quite limited. In this study, we developed a portable whole-plant on-device phenotyping smartphone application running on Android that can measure up to 45 traits, including 15 plant traits, 25 leaf traits and 5 stem traits, based on images. To avoid the influence of outdoor environments, we trained a DeepLabV3+ model for segmentation. In addition, an angle calibration algorithm was also designed to reduce the error introduced by the different imaging angles. The average execution time for the analysis of a 20-million-pixel image is within 2,500 ms. The application is a portable on-device fast phenotyping platform providing methods for real-time trait measurement, which will facilitate maize phenotyping in field and benefit crop breeding in future. |
format | Online Article Text |
id | pubmed-8656718 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-86567182021-12-10 PocketMaize: An Android-Smartphone Application for Maize Plant Phenotyping Liu, Lingbo Yu, Lejun Wu, Dan Ye, Junli Feng, Hui Liu, Qian Yang, Wanneng Front Plant Sci Plant Science A low-cost portable wild phenotyping system is useful for breeders to obtain detailed phenotypic characterization to identify promising wild species. However, compared with the larger, faster, and more advanced in-laboratory phenotyping systems developed in recent years, the progress for smaller phenotyping systems, which provide fast deployment and potential for wide usage in rural and wild areas, is quite limited. In this study, we developed a portable whole-plant on-device phenotyping smartphone application running on Android that can measure up to 45 traits, including 15 plant traits, 25 leaf traits and 5 stem traits, based on images. To avoid the influence of outdoor environments, we trained a DeepLabV3+ model for segmentation. In addition, an angle calibration algorithm was also designed to reduce the error introduced by the different imaging angles. The average execution time for the analysis of a 20-million-pixel image is within 2,500 ms. The application is a portable on-device fast phenotyping platform providing methods for real-time trait measurement, which will facilitate maize phenotyping in field and benefit crop breeding in future. Frontiers Media S.A. 2021-11-25 /pmc/articles/PMC8656718/ /pubmed/34899792 http://dx.doi.org/10.3389/fpls.2021.770217 Text en Copyright © 2021 Liu, Yu, Wu, Ye, Feng, Liu and Yang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Liu, Lingbo Yu, Lejun Wu, Dan Ye, Junli Feng, Hui Liu, Qian Yang, Wanneng PocketMaize: An Android-Smartphone Application for Maize Plant Phenotyping |
title | PocketMaize: An Android-Smartphone Application for Maize Plant Phenotyping |
title_full | PocketMaize: An Android-Smartphone Application for Maize Plant Phenotyping |
title_fullStr | PocketMaize: An Android-Smartphone Application for Maize Plant Phenotyping |
title_full_unstemmed | PocketMaize: An Android-Smartphone Application for Maize Plant Phenotyping |
title_short | PocketMaize: An Android-Smartphone Application for Maize Plant Phenotyping |
title_sort | pocketmaize: an android-smartphone application for maize plant phenotyping |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8656718/ https://www.ncbi.nlm.nih.gov/pubmed/34899792 http://dx.doi.org/10.3389/fpls.2021.770217 |
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