Root mixture analysis: methods and vision

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43410%2F25%3A43927365" target="_blank" >RIV/62156489:43410/25:43927365 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1016/j.tplants.2025.07.003" target="_blank" >https://doi.org/10.1016/j.tplants.2025.07.003</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.tplants.2025.07.003" target="_blank" >10.1016/j.tplants.2025.07.003</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Root mixture analysis: methods and vision

Popis výsledku v původním jazyce

Supporting sustainable agriculture requires a deeper understanding of belowground interactions under diversified crop mixtures. Current tools do not allow differentiation of root species without destructive sampling. This makes the study of crop mixtures and their belowground interactions laborious, leading to a reduction in the scale of research. On the basis of our in-depth review, there is an urgent need for standardized, cost-effective methods for root phenotyping, particularly under field conditions where high variability and logistical difficulties are common. Physicochemical root traits related to root function offer distinctive markers that can represent a species&apos; identity. Processing and analyzing such a unique root data type with optimized deep learning and machine learning can lead to high-throughput root mixture analysis.

Název v anglickém jazyce

Root mixture analysis: methods and vision

Popis výsledku anglicky

Supporting sustainable agriculture requires a deeper understanding of belowground interactions under diversified crop mixtures. Current tools do not allow differentiation of root species without destructive sampling. This makes the study of crop mixtures and their belowground interactions laborious, leading to a reduction in the scale of research. On the basis of our in-depth review, there is an urgent need for standardized, cost-effective methods for root phenotyping, particularly under field conditions where high variability and logistical difficulties are common. Physicochemical root traits related to root function offer distinctive markers that can represent a species&apos; identity. Processing and analyzing such a unique root data type with optimized deep learning and machine learning can lead to high-throughput root mixture analysis.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10611 - Plant sciences, botany

Projekt

—

Návaznosti

O - Projekt operacniho programu

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

Trends in Plant Science

ISSN

1360-1385

e-ISSN

1878-4372

Svazek periodika

30

Číslo periodika v rámci svazku

9

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

13

Strana od-do

1020-1032

Kód UT WoS článku

001569872500009

EID výsledku v databázi Scopus

2-s2.0-105012718870