基于无人机的多层特征选择改善了干旱地区棉花的含量估计
Fengxiu Li1,2, Chongqi Zhao1,2, Yingjie Ma1,2
1College of Hydraulic and Civil Engineering, Xinjiang Agricultural University, Urumqi, China.
Frontiers in plant science
|August 28, 2025
概括
通过选择关键的遥感功能,使用无人机图像来准确估计棉花的状态. 随机森林模型实现了高准确性,指导干旱地区的精确管理.
科学领域:
- 农业科学
- 遥感技术
- 数据科学
背景情况:
- 对于棉花产量和纤维质量至关重要.
- 从无人机图像中估计棉花植物度 (PNC) 是一个挑战,因为复杂的,冗余的遥感数据.
- 这限制了模型的精确性和可转移性.
研究的目的:
- 通过无人机遥感数据开发一种准确且可转移的棉花PNC估计方法.
- 确定用于PNC估计的最佳机器学习算法和关键光谱特征.
- 为棉花生产中精确管理提供指导.
主要方法:
- 使用Elastic Net和Boruta-SHAP的层次特征选择方案来减少数据的维度.
- 评估了六种机器学习算法在估计棉花PNC方面的性能.
- 现场观测用于验证模型输出和评估动态.
主要成果:
- 五个关键的遥感特征 (Mean_B,Mean_R,NDRE_GOSAVI,NDVI,GRVI) 显著改善了模型的性能.
- 随机森林算法表现出优异的性能,R2值为0.97-0.98和RMSE为0.05-0.08.
- 棉花PNC在整个开发过程中下降,最佳的灌和肥维持了更高的水平.
结论:
- 该研究成功提高了来自无人机图像的棉花PNC估计的准确性和可转移性.
- 这些发现支持使用随机森林和精确管理的精选光谱指数.
- 这项研究提供了通过数据驱动的气战略优化干旱环境中的棉花生产的实际指导.
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