在小麦中优化生物质分区使用基于无人机的高光谱现象和基因组预测:基于内核和机器学习的方法
Sudip Kunwar1, Md Ali Babar2, Diego Jarquin2
1Plant Breeding Graduate Program, University of Florida, Gainesville, FL, United States.
Frontiers in plant science
|March 4, 2026
概括
基于无人机 (UAV) 的超谱数据准确地预测了小麦生物质分区特征. 现象学模型显著优于基因组预测,使得更快的繁殖能够提高谷物产量和弹性.
科学领域:
- 农业科学 农业科学
- 植物生理学 植物生理学
- 遥感 遥感 遥感 遥感
背景情况:
- 优化生物质分区对于可持续的小麦产量改善至关重要,尤其是在环境压力下.
- 像尖端分区指数 (SPI),收获指数 (HI) 和果实效率 (FE) 这样的关键特征很难用手工来表型.
- 了解同化分配对于小麦育种计划至关重要.
研究的目的:
- 评估基于无人机的高光谱反射率数据在预测小麦生物质分区特征方面的潜力.
- 为了比较基因组预测 (GP),现象预测 (PP) 和集成的多原子模型.
- 评估这些方法在小麦育种中进行季节性选择的有用性.
主要方法:
- 利用小麦试验 (2022-2024) 的基于无人机的高光谱数据.
- 开发了基因组预测,现象预测和集成的多原子模型.
- 采用基于内核的BLUP,随机森林回归和部分最小平方回归用于预测能力估计.
主要成果:
- 由现象学驱动的模型在SPI (PA高达0.61),FE (PA高达0.56),谷物/m2 (GN,PA高达0.71) 和谷物产量 (GY,PA高达0.66) 等特征上明显优于GP.
- 超谱数据显示的准确性高于植被指数.
- 多原子集成为GN的预测准确性略有改善 (PA高达0.73).
结论:
- 基于无人机的高光谱表型有效捕捉生物质分区的生理信号.
- 这种方法提供了一个可扩展的,基于数据的方法,用于在小麦育种中进行季节性选择.
- 它有助于优化生物质分区,以提高小麦品种的产量弹性和遗传收益.
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