Root mixture analysis: methods and vision

Result code in 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>
Result on the web
<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>

Result language
angličtina
Original language name
Root mixture analysis: methods and vision
Original language description
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' 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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10611 - Plant sciences, botany

Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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