RPT: An integrated root phenotyping toolbox for segmenting and quantifying root system architecture
文献类型： 外文期刊
第一作者： Shi, Jiawei
作者： Shi, Jiawei;Xie, Shangyuan;Li, Weikun;Wang, Xin;Wang, Jianglin;Chen, Yunyu;Chang, Yongyue;Yang, Wanneng;Shi, Jiawei;Xie, Shangyuan;Li, Weikun;Wang, Xin;Wang, Jianglin;Chen, Yunyu;Chang, Yongyue;Yang, Wanneng;Wang, Xin;Lou, Qiaojun;Lou, Qiaojun
作者机构：
关键词： deep learning; high-throughput phenotyping platform; rice drought resistance; root phenotyping; root system architecture
期刊名称：PLANT BIOTECHNOLOGY JOURNAL （ 影响因子：10.5； 五年影响因子：12.4 ）
ISSN： 1467-7644
年卷期： 2025 年 23 卷 6 期
页码：
收录情况： SCI
摘要： The dissection of genetic architecture for rice root system is largely dependent on phenotyping techniques, and high-throughput root phenotyping poses a great challenge. In this study, we established a cost-effective root phenotyping platform capable of analysing 1680 root samples within 2 h. To efficiently process a large number of root images, we developed the root phenotyping toolbox (RPT) with an enhanced SegFormer algorithm and used it for root segmentation and root phenotypic traits. Based on this root phenotyping platform and RPT, we screened 18 candidate (quantitative trait loci) QTL regions from 219 rice recombinant inbred lines under drought stress and validated the drought-resistant functions of gene OsIAA8 identified from these QTL regions. This study confirmed that RPT exhibited a great application potential for processing images with various sources and for mining stress-resistance genes of rice cultivars. Our developed root phenotyping platform and RPT software significantly improved high-throughput root phenotyping efficiency, allowing for large-scale root trait analysis, which will promote the genetic architecture improvement of drought-resistant cultivars and crop breeding research in the future.
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