在各种生长阶段使用无人机超光谱遥感和机器学习估计马树冠叶含水量
Faxu Guo1, Quan Feng1, Sen Yang1
1College of Mechanical and Electrical Engineering, Gansu Agriculture University, Lanzhou, China.
Frontiers in plant science
|November 29, 2024
概括
这项研究使用无人机超光谱遥感来准确监测马叶的水含量 (LWC),以实现高效的灌,这对于在水资源短缺的情况下的粮食安全至关重要. 为不同的生长阶段开发了最佳模型,从而实现了精确的水资源管理.
科学领域:
- 农业科学 农业科学
- 遥感技术 遥感技术 遥感技术
- 植物生理学 植物生理学
背景情况:
- 由于缺水,需要有效的农业灌,以确保粮食安全.
- 准确监测作物水的状态对于可持续农业至关重要.
- 无人机超光谱遥感显示大规模农作物水含量监测具有前景.
研究的目的:
- 开发和验证使用无人机高光谱数据估计土豆叶子含水量 (LWC) 的模型.
- 确定最佳的光谱带和建模技术,以在不同的土豆生长阶段估计LWC.
- 为精确的土豆灌策略提供见解.
主要方法:
- 收集了土豆 (Solanum tuberosum) 结核形成,生长和粉积累期间的超谱和LWC数据.
- 将数学转换 (MSC,SNV) 应用到超光谱数据上.
- 选择使用CARS和RF的特征光谱带.
- 开发了使用PLSR,SVR和BP的LWC估计模型,具有完整和特征频段.
主要成果:
- MSC和SNV转换改善了光谱数据和LWC的相关性.
- 根据生长阶段的最佳LWC估计模型有所不同:MSC-CARS-SVR (菌形成),SNV-CARS-PLSR (菌生长),MSC-RF-PLSR (粉积累).
- 所有最佳模型都显示出出色的预测性能 (RPD>2).
结论:
- 无人机超光谱遥感,结合优化数据处理和建模,为土豆提供准确的LWC估计.
- 开发的模型允许在关键生长阶段精确监测土豆水的状态.
- 这项技术支持节水灌和可持续的土豆生产的数据驱动决策.
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