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Developing a Deep Learning network " MSCP-Net " to generate stalk anatomical traits related with crop lodging and yield in maize

文献类型： 外文期刊

作者： Zhou, Haiyu 1 ; Li, Xiang 2 ; Jiang, Yufeng 3 ; Zhu, Xiaoying 4 ; Fu, Taiming 2 ; Yang, Mingchong 1 ; Cheng, Weidong 3 ; Xie, Xiaodong 3 ; Chen, Yan 2 ; Wang, Lingqiang 1 ;

作者机构： 1.Guangxi Univ, Coll Agr, State Key Lab Conservat & Utilizat Subtrop Agrobio, Nanning 530004, Guangxi, Peoples R China

2.Guangxi Univ, Sch Comp & Elect Informat, Guangxi Key Lab Multimedia Commun Network Technol, Nanning 530004, Guangxi, Peoples R China

3.Guangxi Acad Agr Sci, Maize Res Inst, Nanning 530007, Guangxi, Peoples R China

4.Wuzhou Univ, Key Lab Profess Software Techn, Guangxi Coll & Univ, Wuzhou 543002, Guangxi, Peoples R China

关键词： Maize stalk; Vascular bundle; Deep Learning; Lodging resistance; Yield

期刊名称：EUROPEAN JOURNAL OF AGRONOMY （ 影响因子：5.5； 五年影响因子：5.9 ）

ISSN： 1161-0301

年卷期： 2024 年 160 卷

页码：

收录情况： SCI

摘要： Plant stem is essential for the delivery of resources and has a great impact on plant lodging resistance and yield. However, how to accurately and efficiently extract structural information from crop stems is a big headache. In this study, we first established a Maize Stalk Cross-section Phenotype (MSCP) dataset containing anatomical information of 990 images from hand-cut transections of stalks. Then, to large-scale measure the stalk anatomy features, we developed a Maize Stalk Cross-section Phenotyping Network (MSCP-Net) which integrated a convolutional neural network and the methods of instance segmentation and key point detection. A total of 14 stalk anatomical parameters (traits) can be automatically produced with high mAP@.5 (0.907) for the parameter "vascular bundles segmentation" and high DICE (0.864) for the parameter "functional zones segmentation". The cross-validation with the MSCP dataset indicated the good performance of MSCP-Net in predicting anatomical traits. On this basis, the correlation analysis across 14 anatomical traits and 12 agronomic importance traits in 110 maize inbred-lines was conducted and revealed that the stalk related traits (stem cross-section, large vascular bundles, fiber contents, and aerial roots) are key indicators for lodging resistance and grain yield of maize. In addition, the maize inbred-lines were classified into two groups, and the higher value of group II compared with group I in breeding hybrid varieties was discussed. The results demonstrated that the MSCP-Net is expected to be a useful tool to rapidly obtain stem anatomical traits which are agronomic important in maize genetic improvement.

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