根据特征选择,远程感知反转了化玉米植物中的含量
Kejing Cheng1,2, Jixuan Yan1,2, Guang Li2,3
1College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou, China.
Frontiers in plant science
|March 21, 2025
概括
在玉米中精确的管理是至关重要的. 这项研究使用遥感和机器学习准确估计树冠含量,提高肥料效率和减少对环境的影响.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 数据科学数据科学数据科学
背景情况:
- 过度的气应用和较低的气使用效率是中国农业面临的重大挑战.
- 对于作物生长,产量和质量至关重要,需要精确的管理.
研究的目的:
- 通过使用多谱遥感数据,为玉米开发精确的树冠含量倒置模型.
- 通过精确的施肥,提高使用效率,减少环境污染.
主要方法:
- 采用多谱遥感图像和现场测量的含量.
- 开发并比较了三种树冠含量倒置模型:反向传播神经网络 (BP),支向量机 (SVM) 和部分最小平方回归 (PLSR).
- 利用特征选择来消除冗余的光谱信息,并确定了关键的光谱指数 (GI,NRI) 和频段 (绿色,红色).
主要成果:
- 特征选择通过删除冗余的光谱指数来提高建模效率.
- 绿色指数 (GI) 和反射指数 (NRI) 与玉米树冠中含量有很强的相关性.
- 随机森林 (RF) 算法与PLSR相结合,实现了卓越的预测性能,比独立的PLSR提高3.5%-6.5%的准确性.
结论:
- 多光谱遥感与RF-PLSR等先进算法相结合,使得玉米中精确的诊断成为可能.
- 基于准确的树冠含量估计,可以制定优化管理策略.
- 这种方法为可持续的玉米种植和肥料管理提供了科学基础.
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