[基于UAV-LiDAR的高通量工厂树冠叶面积指数反转模型]
Yuming Liang1, Xueyan Fan1, Muqing Zhang2,3
1School of Electrical Engineering, Guangxi University, Nanning 530004, Guangxi, China.
Sheng wu gong cheng xue bao = Chinese journal of biotechnology
|November 7, 2025
概括
无人驾驶飞行器光检测和测距 (UAV-LiDAR) 有效地测量了甘叶面积指数 (LAI). 使用特定的树冠特征的AdaBoost模型,实现了高精度的作物管理.
科学领域:
- 农业遥感 农业遥感
- 精准农业 精准农业 精准农业
- 农作物的生物物理参数估计估计.
背景情况:
- 精确测量叶面积指数 (LAI) 对于作物管理和育种至关重要.
- 传统的LAI测量方法是劳动密集型和耗时的.
- 无人驾驶飞行器光检测和测距 (UAV-LiDAR) 为高通量天花板分析提供了一个潜在的解决方案.
研究的目的:
- 评估使用UAV-LiDAR用于估计甘LAI的可行性.
- 为了从UAV-LiDAR数据中确定LAI逆转的最佳特征.
- 使用机器学习算法开发和验证准确的LAI估计模型.
主要方法:
- 在耕作和延长阶段,UAV-LiDAR采集了甘树冠的数据.
- 取出树冠特征,包括行高度,预计的行面积和点云密度.
- 应用特征选择方法 (PLSR,XGBoost-FI,RF-RFE) 来识别关键变量.
- 使用随机森林 (RF) 和自适应增强 (AdaBoost) 算法开发 LAI 倒置模型.
主要成果:
- 预计的行面积 (Sp) 和总行点数 (Ctotal) 与LAI有很强的相关性 (r=0.73和r=0.72).
- 使用Sp,平均高度 (Havg),中层点云密度 (Cm) 和Ctotal的AdaBoost模型实现了最高的性能.
- 验证结果显示确定系数 (Rv2) 为0.713和RMSEv为0.25.
结论:
- 无人机LiDAR是一种可行且有效的技术,用于在甘中进行高通量LAI估计.
- 精选的LiDAR衍生功能和机器学习模型可以准确预测作物LAI.
- 这种方法为精确的甘管理和育种计划提供了宝贵的支持.
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