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基于深度学习和X射线计算机断层扫描在大麦中的尖端架构的检测和表征

Yimin Ling¹, Qinlong Zhao¹, Wenxin Liu¹

¹State Key Laboratory of Crop Stress Biology in Arid Areas and College of Agronomy, Northwest A&F University, Yangling, 712100, Shaanxi, China.

|October 27, 2023

概括

我们开发了一种使用X射线CT和深度学习来分析大麦尖特征的非侵入性方法. 这种高通量表型化工具准确地量化了大麦架构,以改善作物育种.

科学领域:

农业科学农业科学
植物生物学植物生物学
计算生物学计算生物学

背景情况:

麦尖对于确定谷物产量和质量至关重要.
精确测量尖特征对于作物改进至关重要.
目前分析大麦尖形态的方法,特别是使用CT扫描,是有限的.

研究的目的:

开发一种非侵入性,高通量方法,对大麦的建筑特征进行定量分析.
将X射线计算机断层扫描 (CT) 与深度学习模型 (UNet) 结合起来,用于虚拟尖峰重建.
为剖析大麦尖发育的遗传基础奠定基础.

主要方法:

来自不同种类 (野生,陆地品种,栽培品种) 的大麦的X射线CT扫描.
用CT数据进行3D图像处理,从点云数据中生成"虚拟"峰值.
应用深度学习模型 (UNet) 用于特征提取和特征量化.

主要成果:

从CT扫描中成功重建了3D"虚拟"大麦尖.
量化多个尖峰特征非破坏性,包括尖峰长度,粒数,粒量,表面积,长度和宽度.
在虚拟和手动测量的特征之间显示出高度一致性,证实了模型的准确性和可靠性.

结论:

这项研究提出了一种高效的,非侵入性的工具,用于剖析大麦尖架构.
开发的方法有助于大麦的高通量表型化.
这种方法将有助于高产大麦和其他谷物作物的育种计划.

关键词:

3D形态处理3D形态处理大麦大麦,大麦大麦,大麦大麦,大麦大麦.计算建模计算建模斯派克建筑的建筑.

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