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.

中文翻译：

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开发深度学习网络“MSCP-Net”以生成与玉米倒伏和产量相关的茎秆解剖特征


植物茎对于资源的输送至关重要，对植物的抗倒伏和产量影响很大。然而，如何准确、高效地从农作物茎中提取结构信息是一个令人头疼的问题。在本研究中，我们首先建立了玉米茎横截面表型 (MSCP) 数据集，其中包含来自茎的手工切割横切面的 990 张图像的解剖信息。然后，为了大规模测量茎的解剖特征，我们开发了玉米茎横截面表型网络（MSCP-Net），它集成了卷积神经网络以及实例分割和关键点检测的方法。总共14个茎解剖参数（性状）可以自动生成，参数“维管束分割”具有高mAP@.5（0.907），参数“功能区分割”具有高DICE（0.864）。与 MSCP 数据集的交叉验证表明 MSCP-Net 在预测解剖特征方面具有良好的性能。在此基础上，对110个玉米自交系的14个解剖性状和12个农艺重要性状进行了相关分析，发现茎相关性状（茎横断面、大维管束、纤维含量和气生根）是关键玉米抗倒伏和产量指标。此外，将玉米自交系分为两组，并讨论了组II较组I在选育杂交品种中的更高价值。结果表明，MSCP-Net 有望成为快速获得茎解剖性状的有用工具，这些性状对玉米遗传改良具有重要的农艺性状。