Machine learning for image-based multi-omics analysis of leaf veins

Bibliographische Detailangaben
Veröffentlicht in:	Journal of experimental botany. - 1985. - 74(2023), 17 vom: 13. Sept., Seite 4928-4941
1. Verfasser:	Zhang, Yubin (VerfasserIn)
Weitere Verfasser:	Zhang, Ning, Chai, Xiujuan, Sun, Tan
Format:	Online-Aufsatz
Sprache:	English
Veröffentlicht:	2023
Zugriff auf das übergeordnete Werk:	Journal of experimental botany
Schlagworte:	Review Journal Article Research Support, Non-U.S. Gov't Deep learning enviromics analysis growth prediction model image analysis multi-omics analysis phenotype omics vein network

Beschreibung
Zusammenfassung:	© The Author(s) 2023. Published by Oxford University Press on behalf of the Society for Experimental Biology. All rights reserved. For permissions, please email: journals.permissionsoup.com. Veins are a critical component of the plant growth and development system, playing an integral role in supporting and protecting leaves, as well as transporting water, nutrients, and photosynthetic products. A comprehensive understanding of the form and function of veins requires a dual approach that combines plant physiology with cutting-edge image recognition technology. The latest advancements in computer vision and machine learning have facilitated the creation of algorithms that can identify vein networks and explore their developmental progression. Here, we review the functional, environmental, and genetic factors associated with vein networks, along with the current status of research on image analysis. In addition, we discuss the methods of venous phenotype extraction and multi-omics association analysis using machine learning technology, which could provide a theoretical basis for improving crop productivity by optimizing the vein network architecture
Beschreibung:	Date Completed 14.09.2023 Date Revised 18.09.2023 published: Print Citation Status MEDLINE
ISSN:	1460-2431
DOI:	10.1093/jxb/erad251