Crop height and density estimation ahead of the combine harvester have been investigated over the last 30 years, but they remain a challenge. LiDAR technology is increasingly being investigated for phenotyping and monitoring of cereals. However, so far, little has been published about the influence of laser mounting position and robust online measurement of height and density from a single LiDAR scan. Therefore, the influence of the angle and height of the LiDAR mounting on crop height and density estimation in wheat and barley was investigated in this study. Tests were conducted in different crop heights, densities, moisture levels and varieties. The crop height was estimated with a root mean squared error of 0.082 m at an angle of 90° between the LiDAR scanning plane and the horizontal. Two methods were compared for crop density estimation. The inter-percentile and the transversal variance method both successfully predicted the crop density, but the variance based method performed better with a coefficient of determination (R2) of 0.77 and a root mean squared error of 82 g [dry ears] m-2. When considering only one variety, the performance improved to reach an R2 of 0.8 and a root mean squared error of 44 g [dry ears] m-2. Variation in mounting angle of the sensor had less effect on the prediction accuracy than the mounting height.

中文翻译：

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使用激光雷达在线估计粮田作物高度和密度

在过去的 30 年里，联合收割机之前的作物高度和密度估计已经被研究，但它们仍然是一个挑战。LiDAR 技术越来越多地被研究用于谷物的表型分析和监测。然而，到目前为止，关于激光安装位置的影响以及通过单次 LiDAR 扫描对高度和密度进行稳健的在线测量的报道很少。因此，本研究研究了 LiDAR 安装的角度和高度对小麦和大麦作物高度和密度估计的影响。测试是在不同的作物高度、密度、水分含量和品种下进行的。在 LiDAR 扫描平面和水平面之间的角度为 90° 时，作物高度的估计均方根误差为 0.082 m。比较了两种用于作物密度估计的方法。百分间距和横向方差方法都成功地预测了作物密度，但基于方差的方法表现更好，决定系数 (R2) 为 0.77，均方根误差为 82 g [干穗] m-2。当仅考虑一种品种时，性能提高到 R2 为 0.8，均方根误差为 44 g [干穗] m-2。与安装高度相比，传感器安装角度的变化对预测精度的影响较小。8 和 44 g [干耳] m-2 的均方根误差。与安装高度相比，传感器安装角度的变化对预测精度的影响较小。8 和 44 g [干耳] m-2 的均方根误差。与安装高度相比，传感器安装角度的变化对预测精度的影响较小。