植物内源性状态的分类使用机器学习衍生的农业指数
Sally Shuxian Koh1,2, Kapil Dev3, Javier Jingheng Tan1
1Temasek Life Sciences Laboratory, National University of Singapore, Singapore, Singapore.
Plant phenomics (Washington, D.C.)
|June 29, 2023
概括
这项研究引入了一种使用可见近红外短波红外 (VIS-NIR-SWIR) 光谱和机器学习的新方法,以准确诊断植物健康. 该方法在干旱条件下有效地识别了与压力激素酸 (ABA) 相关的生理变化.
科学领域:
- 植物生理学 植物生理学
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 叶子颜色的变化表明植物的压力,但目前的光谱分析往往缺乏特异性.
- 可见近红外短波红外光谱 (VIS-NIR-SWIR) 为植物健康评估提供高分辨率的光谱数据.
- 使用光谱数据的现有方法主要评估一般植物健康或色素含量,而不是特定的代谢途径.
研究的目的:
- 开发特征工程和机器学习方法,以使用VIS-NIR-SWIR叶子反射率进行强大的植物健康诊断.
- 用光谱数据确定与压力激素酸 (ABA) 相关的生理变化.
- 为了区分与干旱压力和ABA缺陷相关的光谱特征.
主要方法:
- 在灌和干旱条件下从野生类型,ABA2过度表达和ABA缺乏的植物收集了叶子反射光谱.
- 从所有可能的波长带对中选干旱和ABA相关的正常化反射率指数 (NRIs).
- 开发了可解释的支持向量机分类器,使用选定的NRIs来预测治疗和基因型组.
主要成果:
- 与干旱相关的规范化反射率指数 (NRIs) 与与ABA缺乏相关的部分重叠.
- 更多的NRIs与干旱压力有关,包括NIR范围的光谱变化.
- 使用20个NRIs构建的分类器实现了比使用传统植被指数的分类器更高的准确性.
- 发现的关键NRIs与叶子水和叶绿素含量无关.
结论:
- 具有机器学习的VIS-NIR-SWIR光谱的特征工程使得精确的植物健康诊断成为可能.
- 开发的NRIs有效地确定了与酸 (ABA) 和干旱压力相关的生理变化.
- 这种方法提供了一种更有效的方法来检测与特定植物应激特征相关的光谱带.
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