豆 (Glycine max L.) 叶子湿度估计基于多源无人机图像特征融合
Wanli Yang1,2, Zhijun Li1,2, Guofu Chen1,2
1Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas of Ministry of Education, Northwest A&F University, Xianyang 712100, China.
Plants (Basel, Switzerland)
|June 19, 2024
概括
无人驾驶飞行器多谱技术有效监测大豆叶的湿度. 将植被指数,纹理特征和随机指数与XGBoost相结合,实现了作物水评估的最高准确性.
科学领域:
- 农业遥感 农业遥感
- 精准农业 精准农业 精准农业
- 植物生理学作物生理学
背景情况:
- 准确的作物叶湿度数据对于有效的灌管理和最大限度地提高作物产量至关重要.
- 无人驾驶飞行器 (UAV) 多谱技术提供了一种非破坏性的方法来获取作物信息.
研究的目的:
- 评估无人机多光谱技术的有效性,用于监测大豆叶的水分含量.
- 为了比较不同的输入变量组合和机器学习模型来估计叶子的湿度.
主要方法:
- 实地实验收集了大豆叶湿度和UAV多光谱数据超过两年 (2021-2022).
- 植被指数,树冠纹理特征和随机纹理指数被提取并分析与叶子水分的相关性.
- 使用了极端学习机器 (ELM),极端梯度增强 (XGBoost) 和反向传播神经网络 (BPNN) 模型.
主要成果:
- 植被指数通常与叶子水分的相关性比纹理特征更高.
- 随机提取的纹理指数改善了相关性,RDTI显示了最高的个人相关性 (0.683).
- 使用植被指数,纹理特征和随机纹理指数组合的XGBoost模型在验证集中获得了最高的R2 0.816.
结论:
- 无人机多谱监测是评估大豆叶湿度的可行方法.
- 将多样化的光谱和纹理特征与先进的机器学习模型 (如XGBoost) 结合起来,可以提高监测的准确性.
- 这项研究为改善农业作物水状况评估和管理提供了基础.
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