选择最佳的光谱特征来估计叶子中的叶绿素含量
Yangyang Zhang1, Xu Han1, Jian Yang2
1College of Civil Engineering, Wuhan City Polytechnic, Wuhan, 430074, Hubei, China.
Scientific reports
|October 28, 2024
概括
准确估计叶子中的叶绿素含量 (LCC) 对作物监测至关重要. 该研究发现,将特定的光谱特征与高斯过程回归 (GPR) 结合起来,为各种条件中精确的LCC估计提供了最佳方法.
科学领域:
- 农业遥感 农业遥感
- 植物生理学 植物生理学
- 机器学习在农业中的应用
背景情况:
- 叶子中的叶绿素含量 (LCC) 是作物生理状态和健康的关键指标.
- 准确的LCC估计对于定量作物生长评估和管理至关重要.
- 频谱特征和回归算法显著影响LCC估计精度.
研究的目的:
- 为了确定LCC估计的最佳光谱特征.
- 评估机器学习方法在不同作物,现象学阶段和传感器中的一致性.
- 确定光谱特征和回归算法的最佳组合,以准确估计LCC.
主要方法:
- 提取各种光谱特征:原始,衍生,连续移除,主要组成变量和相关特征.
- 在各种数据集上使用六个回归算法构建LCC估计模型.
- 对考虑作物类型,表态和传感器的最佳特征-算法组合的分析.
主要成果:
- 具有顶部相关性 (PCA_CRDR_R) 的连续移除衍生反射率的主要组成变量成为最佳光谱特征.
- 高斯过程回归 (GPR) 在与PCA_CRDR_R.R.相结合时表现出卓越的性能.
- 在使用最佳组合来估计甜菜LCC时,获得了0.62的R2.
结论:
- PCA_CRDR_R 功能集与 GPR 结合,提供了一种可靠的方法来进行天花板规模的 LCC 估计.
- 这种方法为远程传感应用中选择光谱特征和算法提供了有价值的理论指导.
- 这些发现支持通过精确的LCC评估改进作物监测和管理策略.
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