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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