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@article{2235888,
author = {Narisetti, Narendra and Henke, Michael and Neumann, Kerstin and Stolzenburg, Frieder and Altmann, Thomas and Gladilin, Evgeny},
article_location = {Lausanne},
article_number = {JUL},
doi = {http://dx.doi.org/10.3389/fpls.2022.906410},
keywords = {greenhouse image analysis; image segmentation; deep learning; U-net; quantitative plant phenotyping},
language = {eng},
issn = {1664-462X},
journal = {Frontiers in Plant Science},
title = {Deep Learning Based Greenhouse Image Segmentation and Shoot Phenotyping (DeepShoot)},
url = {https://www.frontiersin.org/articles/10.3389/fpls.2022.906410/full},
volume = {13},
year = {2022}
}
TY - JOUR
ID - 2235888
AU - Narisetti, Narendra - Henke, Michael - Neumann, Kerstin - Stolzenburg, Frieder - Altmann, Thomas - Gladilin, Evgeny
PY - 2022
TI - Deep Learning Based Greenhouse Image Segmentation and Shoot Phenotyping (DeepShoot)
JF - Frontiers in Plant Science
VL - 13
IS - JUL
SP - 1-16
EP - 1-16
PB - FRONTIERS MEDIA SA
SN - 1664462X
KW - greenhouse image analysis
KW - image segmentation
KW - deep learning
KW - U-net
KW - quantitative plant phenotyping
UR - https://www.frontiersin.org/articles/10.3389/fpls.2022.906410/full
N2 - BackgroundAutomated analysis of large image data is highly demanded in high-throughput plant phenotyping. Due to large variability in optical plant appearance and experimental setups, advanced machine and deep learning techniques are required for automated detection and segmentation of plant structures in complex optical scenes. MethodsHere, we present a GUI-based software tool (DeepShoot) for efficient, fully automated segmentation and quantitative analysis of greenhouse-grown shoots which is based on pre-trained U-net deep learning models of arabidopsis, maize, and wheat plant appearance in different rotational side- and top-views. ResultsOur experimental results show that the developed algorithmic framework performs automated segmentation of side- and top-view images of different shoots acquired at different developmental stages using different phenotyping facilities with an average accuracy of more than 90% and outperforms shallow as well as conventional and encoder backbone networks in cross-validation tests with respect to both precision and performance time. ConclusionThe DeepShoot tool presented in this study provides an efficient solution for automated segmentation and phenotypic characterization of greenhouse-grown plant shoots suitable also for end-users without advanced IT skills. Primarily trained on images of three selected plants, this tool can be applied to images of other plant species exhibiting similar optical properties.
ER -
NARISETTI, Narendra, Michael HENKE, Kerstin NEUMANN, Frieder STOLZENBURG, Thomas ALTMANN a Evgeny GLADILIN. Deep Learning Based Greenhouse Image Segmentation and Shoot Phenotyping (DeepShoot). \textit{Frontiers in Plant Science}. Lausanne: FRONTIERS MEDIA SA, 2022, roč.~13, JUL, s.~1-16. ISSN~1664-462X. Dostupné z: https://dx.doi.org/10.3389/fpls.2022.906410.
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