通过超光谱转换和机器学习来预测狼树的叶子含量,用于精准农业
Yongmei Li1,2, Hao Wang1,3,4, Hongli Zhao3
1School of Civil and Hydraulic Engineering, NingXia university, Yinchuan, Ningxia, People's Republic of China.
PloS one
|September 26, 2024
概括
精确预测狼树的叶子含量 (LNC) 对作物健康至关重要. 这项研究开发了新的光谱指数和机器学习模型,发现与LNC估计相结合的第一个导数转换和随机森林是最佳的.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 植物生理学 植物生理学
背景情况:
- 叶子含量 (LNC) 是作物营养状况的关键指标,影响生长,产量和质量.
- 准确的LNC评估对于优化狼种植和管理至关重要.
研究的目的:
- 开发新的光谱指数 (NSI) 来预测狼树LNC.
- 使用各种光谱转换和索引构建和比较机器学习回归 (MLR) 模型.
主要方法:
- 在光谱数据上使用了四种光滑和五种数学转换方法.
- 选敏感波长以创建NSI,并将其与已发布的植被指数 (PVIs) 进行比较.
- 从转换的光谱数据集中开发了使用三个算法 (包括随机森林) 与NSI和PVI的MLR模型.
主要成果:
- 数学转换,特别是平方根,第一导数和第二导数,增强了光谱差异和改善了MLR-NSI模型的准确性.
- 这些转换对MLR-PVI模型预测能力的影响很小.
- 最优的模型结合了随机森林与从第一衍生变换光谱 (Rv2=0.71,RPD=1.90) 衍生的NSI.
结论:
- 超光谱数据转换显著提高了狼树LNC估计的准确性.
- 频谱转换技术和机器学习的整合为精确的LNC监控提供了强大的方法.
- 这种方法为科学诊断和管理狼营养状况提供了有价值的工具.
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