Deep Learning Based Greenhouse Image Segmentation and Shoot Phenotyping (DeepShoot)
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14740%2F22%3A00127315" target="_blank" >RIV/00216224:14740/22:00127315 - isvavai.cz</a>
Result on the web
<a href="https://www.frontiersin.org/articles/10.3389/fpls.2022.906410/full" target="_blank" >https://www.frontiersin.org/articles/10.3389/fpls.2022.906410/full</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3389/fpls.2022.906410" target="_blank" >10.3389/fpls.2022.906410</a>
Alternative languages
Result language
angličtina
Original language name
Deep Learning Based Greenhouse Image Segmentation and Shoot Phenotyping (DeepShoot)
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10600 - Biological sciences
Result continuities
Project
<a href="/en/project/EF16_026%2F0008446" target="_blank" >EF16_026/0008446: Signal integration and epigenetic reprograming for plant productivity</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Frontiers in Plant Science
ISSN
1664-462X
e-ISSN
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Volume of the periodical
13
Issue of the periodical within the volume
JUL
Country of publishing house
CH - SWITZERLAND
Number of pages
16
Pages from-to
1-16
UT code for WoS article
000832787800001
EID of the result in the Scopus database
2-s2.0-85134978144