Computer vision-based recognition and distinction of Arabidopsis thaliana ecotypes using supervised deep learning models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61389030%3A_____%2F25%3A00638992" target="_blank" >RIV/61389030:_____/25:00638992 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1002/ppj2.70041" target="_blank" >https://doi.org/10.1002/ppj2.70041</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/ppj2.70041" target="_blank" >10.1002/ppj2.70041</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Computer vision-based recognition and distinction of Arabidopsis thaliana ecotypes using supervised deep learning models

Popis výsledku v původním jazyce

Image-based plant phenotyping has diverse applications, ranging from providing quantitative traits for genetic breeding to enhancing management practices for indoor and outdoor production systems. Misidentification of cell lines or ecotypes/varieties is a major problem across all biological research disciplines. With the 1000 Arabidopsis Genome Project facilitating the use of various ecotypes, it is crucial to verify the identity of ecotypes in discovery-based genetic screens involving hundreds of ecotypes. To address this issue, an RGB image analysis pipeline was established for the accurate recognition of different Arabidopsis thaliana ecotypes. In the developed pipeline, the most crucial aspects for accurately capturing traits and training deep learning models were identified as follows: (i) assessment of data complexity using spatial-temporal features of the RGB spectrum and data entropy, the latter defined as the variability within the dataset, (ii) data redefinition in instances of high data complexity, and (iii) data partitioning based on extracted morphological similarity among ecotype replicates. The pipeline includes several supervised deep learning models integrated into an auto-optimization subsystem. Extensive hyperparameter tuning was performed to identify the best-performing models for single-image and image-sequence ecotype classification. Two external datasets were evaluated to demonstrate the robustness of the pipeline, regardless of how they were collected. A graphical user interface is provided to prepare these images for input into the pipeline in cases of extreme variability. The pipeline can automatically verify ecotypes in large-scale studies and extract traits for further analysis and correlation, as needed, using datasets from a variety of sources.

Název v anglickém jazyce

Computer vision-based recognition and distinction of Arabidopsis thaliana ecotypes using supervised deep learning models

Popis výsledku anglicky

Image-based plant phenotyping has diverse applications, ranging from providing quantitative traits for genetic breeding to enhancing management practices for indoor and outdoor production systems. Misidentification of cell lines or ecotypes/varieties is a major problem across all biological research disciplines. With the 1000 Arabidopsis Genome Project facilitating the use of various ecotypes, it is crucial to verify the identity of ecotypes in discovery-based genetic screens involving hundreds of ecotypes. To address this issue, an RGB image analysis pipeline was established for the accurate recognition of different Arabidopsis thaliana ecotypes. In the developed pipeline, the most crucial aspects for accurately capturing traits and training deep learning models were identified as follows: (i) assessment of data complexity using spatial-temporal features of the RGB spectrum and data entropy, the latter defined as the variability within the dataset, (ii) data redefinition in instances of high data complexity, and (iii) data partitioning based on extracted morphological similarity among ecotype replicates. The pipeline includes several supervised deep learning models integrated into an auto-optimization subsystem. Extensive hyperparameter tuning was performed to identify the best-performing models for single-image and image-sequence ecotype classification. Two external datasets were evaluated to demonstrate the robustness of the pipeline, regardless of how they were collected. A graphical user interface is provided to prepare these images for input into the pipeline in cases of extreme variability. The pipeline can automatically verify ecotypes in large-scale studies and extract traits for further analysis and correlation, as needed, using datasets from a variety of sources.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10611 - Plant sciences, botany

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

PLANT PHENOME JOURNAL

ISSN

2578-2703

e-ISSN

2578-2703

Svazek periodika

8

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

23

Strana od-do

e70041

Kód UT WoS článku

001556711300001

EID výsledku v databázi Scopus

2-s2.0-105014107973