估计植物中含量的估计,基于多源特征融合
Jiapeng Zhu1, Haohao Dang1, Demin Fu1
1College of Water Conservancy and Hydrpower Engineering, Gansu Agricultural University, Lanzhou 730070, China.
Plants (Basel, Switzerland)
|March 14, 2026
概括
使用多光谱图像和机器学习准确估计中的植物含量 (PNC),可以显著改善作物管理. 结合植被指数和纹理指数,可以提高预测准确度,从而提高肥料的效率,减少污染.
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:
- 植物含量 (PNC) 对于评估作物营养和优化肥料使用至关重要.
- 精确的PNC监测有助于诊断作物健康状况,提高肥料效率,并尽量减少农业污染.
研究的目的:
- 评估多谱植被指数 (VI) 和来自无人机图像的纹理指数 (TI) 的有效性,以估计PNC.
- 为了比较四个机器学习模型 (RFR,SVR,BPNN,XG-Boost) 在使用VI和TI预测花PNC的性能.
主要方法:
- 从UAV多光谱图像中提取的VI和纹理特征值 (TFV) 在关键的生长阶段.
- 通过结合TFV和确定与PNC高度相关的变量来构建TI.
- 训练并验证了四个机器学习模型,使用VI,TI及其PNC估计的组合特性.
主要成果:
- 试验结果显示,与子PNC的相关性比原始质地值更强 (天草 > 0.6).
- 整合VI和TI显著提高了跨增长阶段的PNC估计准确性 (R2增加了5.4-19.7%).
- 结合VI和TI的XG-Boost模型实现了最高的准确性,在芽阶段的验证R2为0.80.
结论:
- 从无人机图像中获得的多谱VI和TI的整合对于准确的草PNC估计是有效的.
- 这种方法为精确的田间管理和肥策略提供了有价值的科学支持.
- 加强PNC监测有助于高效的管理和减少对环境的影响.
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