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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • 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

  • 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