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SeedGerm-VIG:一个开放和全面的管道,用深度学习驱动的动态表型分析量化小麦和其他谷物作物的种子活力

Jie Dai¹, Zhenjie Wen¹, Mujahid Ali¹

¹College of Engineering, College of Agriculture, Academy for Advanced Interdisciplinary Studies, Plant Phenomics Research Centre, Nanjing Agricultural University, Nanjing 210095, China.

|October 16, 2025

概括

我们开发了SeedGerm-VIG,这是一个使用深度学习评估小麦种子活力和关键生长特征的自动化管道. 该系统准确地测量了发芽速度和均性,有助于作物改进研究.

科学领域:

农业学和作物科学农业学和作物科学
植物育种植物育种
计算生物学计算生物学

背景情况:

小麦 (Triticum aestivum L.) 是一个重要的全球谷物作物.
早期的生长阶段,如发芽和苗木的建立,显著影响小麦的产量潜力.
开发具有优越的早期生长特征的品种对于各种环境至关重要.

研究的目的:

引入SeedGerm-VIG,这是一个自动化管道,用于在谷物中进行全面的种子活力评估.
利用深度学习和计算机视觉进行种子发芽和苗木特征的详细分析.
为评估商业小麦基因型建立一个新的活力评分矩阵.

主要方法:

集成深度学习模型 (YOLOv8x-Germ,U-Net) 和计算机视觉算法用于自动化分析.
使用时间序列定向图来追踪根的出现,并测量发芽速度和统一性.
在多个发芽阶段的种子,根和苗级评估表型特征.

主要成果:

SeedGerm-VIG准确地测量了根的出现 (R2 = 84.1%),并评估了发芽速度和均性.
创建了一个活力评分矩阵,根据超过29,500张图像评估了21种小麦基因型.
结果与按照ISTA指导方针进行的手册评估有很高的一致性,并且在大米和大麦中证明了可重复性.

结论:

SeedGerm-VIG提供了一种自动化,可扩展的解决方案,用于检查植物中的种子活力和相关特征.
这种工具促进了有效的植物选择,并促进了种子科学研究以改善作物.
该管道支持植物和作物研究社区的更广泛应用.

关键词:

动态特征分析动态特征分析发芽发芽发芽是一个过程.种子活力,种子活力.基于视觉的深度学习小麦小麦小麦小麦小麦小麦小麦.

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