Deep Learning Based Greenhouse Image Segmentation and Shoot Phenotyping (DeepShoot)
Identifikátory výsledku
Kód výsledku v 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>
Výsledek na webu
<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>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Deep Learning Based Greenhouse Image Segmentation and Shoot Phenotyping (DeepShoot)
Popis výsledku v původním jazyce
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.
Název v anglickém jazyce
Deep Learning Based Greenhouse Image Segmentation and Shoot Phenotyping (DeepShoot)
Popis výsledku anglicky
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.
Klasifikace
Druh
J<sub>imp</sub> - Článek v periodiku v databázi Web of Science
CEP obor
—
OECD FORD obor
10600 - Biological sciences
Návaznosti výsledku
Projekt
<a href="/cs/project/EF16_026%2F0008446" target="_blank" >EF16_026/0008446: Integrace signálu a epigenetické reprogramování pro produktivitu rostlin</a><br>
Návaznosti
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2022
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Frontiers in Plant Science
ISSN
1664-462X
e-ISSN
—
Svazek periodika
13
Číslo periodika v rámci svazku
JUL
Stát vydavatele periodika
CH - Švýcarská konfederace
Počet stran výsledku
16
Strana od-do
1-16
Kód UT WoS článku
000832787800001
EID výsledku v databázi Scopus
2-s2.0-85134978144