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

文献类型： 外文期刊

第一作者： Zhang, Yubin

作者： Zhang, Yubin;Zhang, Ning;Chai, Xiujuan;Sun, Tan;Sun, Tan

作者机构：

关键词： Deep learning; enviromics analysis; growth prediction model; image analysis; multi-omics analysis; phenotype omics; vein network

期刊名称：JOURNAL OF EXPERIMENTAL BOTANY （ 影响因子：6.9； 五年影响因子：8.0 ）

ISSN： 0022-0957

年卷期： 2023 年 74 卷 17 期

页码：

收录情况： SCI

摘要： 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. This review proposes a machine learning approach for venous multi-omics analysis using image technology, with the aim of building a multi-omics association model linking genotype, phenotype, and environment.

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