The PhenoLab an automated, high-throughput phenotyping platform for analyzing development, abiotic stress responses and pathogen infection in model and crop plants

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F86652079%3A_____%2F25%3A00618825" target="_blank" >RIV/86652079:_____/25:00618825 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2772375525000784" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2772375525000784</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.atech.2025.100845" target="_blank" >10.1016/j.atech.2025.100845</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The PhenoLab an automated, high-throughput phenotyping platform for analyzing development, abiotic stress responses and pathogen infection in model and crop plants

Popis výsledku v původním jazyce

Important plant stresses are drought, but also biotic stresses caused by pathogens have economically important losses to crops worldwide. Advancements in our ability to fast, sensitive and cost efficient detect stress responses by sensor based imaging are important to improve crop management practices. As a step towards this, we introduce a fully automated, high-throughput plant phenotyping platform called PhenoLab. It automatically ensures precise and automatic irrigation of plants and non-destructively, fast and quantitatively measure biomass, abiotic and biotic stresses via multispectral imaging. A user friendly software for supervised machine learning based spectral image analysis is used for image processing and water consumption of individual plants can be extracted from an integrated database. As a proof of concept, we used two important crop plants for phenotyping and detecting abiotic and biotic stresses. Individual multi-spectral measurements (within 365-970 nm) and vegetation index were considered in the image processing to detect drought symptoms of maize plants. Powdery mildew of barley plants was sufficiently detected and quantified via multi-reflectance and multi-fluorescence image system during disease progression. The integrated settings for multispectral image recording, computer vision and image processing platform with customized settings and protocols are expected as practical importance for academic and translational high-throughput research. It will be notably relevant for more complex systems with additional multiple factors e.g., multiple plant genotypes and their resistance and susceptibility to abiotic and biotic stresses, or treatments of beneficial microbes for sustainable improvement of general stress resiliency.

Název v anglickém jazyce

The PhenoLab an automated, high-throughput phenotyping platform for analyzing development, abiotic stress responses and pathogen infection in model and crop plants

Popis výsledku anglicky

Important plant stresses are drought, but also biotic stresses caused by pathogens have economically important losses to crops worldwide. Advancements in our ability to fast, sensitive and cost efficient detect stress responses by sensor based imaging are important to improve crop management practices. As a step towards this, we introduce a fully automated, high-throughput plant phenotyping platform called PhenoLab. It automatically ensures precise and automatic irrigation of plants and non-destructively, fast and quantitatively measure biomass, abiotic and biotic stresses via multispectral imaging. A user friendly software for supervised machine learning based spectral image analysis is used for image processing and water consumption of individual plants can be extracted from an integrated database. As a proof of concept, we used two important crop plants for phenotyping and detecting abiotic and biotic stresses. Individual multi-spectral measurements (within 365-970 nm) and vegetation index were considered in the image processing to detect drought symptoms of maize plants. Powdery mildew of barley plants was sufficiently detected and quantified via multi-reflectance and multi-fluorescence image system during disease progression. The integrated settings for multispectral image recording, computer vision and image processing platform with customized settings and protocols are expected as practical importance for academic and translational high-throughput research. It will be notably relevant for more complex systems with additional multiple factors e.g., multiple plant genotypes and their resistance and susceptibility to abiotic and biotic stresses, or treatments of beneficial microbes for sustainable improvement of general stress resiliency.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

40101 - Agriculture

Projekt

<a href="/cs/project/LM2023048" target="_blank" >LM2023048: Česká infrastruktura sledování uhlíku</a><br>

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

Smart Agricultural Technology

ISSN

2772-3755

e-ISSN

2772-3755

Svazek periodika

11

Číslo periodika v rámci svazku

AUG

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

15

Strana od-do

100845

Kód UT WoS článku

001448281700001

EID výsledku v databázi Scopus

2-s2.0-105000065958