通过结合多模式深度学习和动态建模,对大豆生长分析进行时间序列现场表型化
Hui Yu1,2, Lin Weng2, Songquan Wu3
1Key Laboratory of Soybean Molecular Design Breeding, State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China.
Plant phenomics (Washington, D.C.)
|March 25, 2024
概括
使用无人驾驶飞行器 (UAV) 的高吞吐量表型化可以快速评估大豆树冠的发展. 一个新的RIFSeg-Net模型准确地识别了大豆品种和树冠特征,改善了生殖质识别和基因型分析.
科学领域:
- 农业科学 农业科学
- 植物育种 植物育种
- 遥感 遥感 遥感 遥感
背景情况:
- 大豆树冠建立率对于产量潜力至关重要,但在大型试验中很难评估.
- 使用无人驾驶飞行器 (UAV) 的高通量表型化为监测大豆树冠发展提供了一个解决方案.
- 为了高效的繁殖,需要对大豆树冠跨基因型的发展进行定量描述.
研究的目的:
- 开发和验证使用无人机数据进行大豆树冠分析的多模式图像细分模型.
- 根据叶子形态学准确分类大豆品种,并确定关键的树冠发育特征.
- 评估与传统深度学习方法相比,开发模型的性能.
主要方法:
- 收集了大豆种群的高分辨率,时间序列RGB和红外无人机图像.
- 开发了RGB和红外特征融合细分网络 (RIFSeg-Net) 用于图像细分.
- 采用细分任何模型来提取单个叶子用于品种分类和树冠特征的动态建模.
主要成果:
- 在提取大豆树冠覆盖的过程中,RIFSeg-Net取得了很高的性能 (精度=0.94,回忆=0.93,F1分数=0.93).
- 成功地将大豆种群分为圆形和形叶类型.
- 确定了5种与品种之间的树冠发育速度相关的5种独特的表型特征.
结论:
- RIFSeg-Net模型在基于无人机的大豆树冠分析的传统方法上提供了显著的进步.
- 这种方法提供了一个实用的工具来识别生殖质资源和基因型差异化.
- 该方法促进了对大豆改进的目标基因的有效选择.
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