Biological Systems Engineering Department, University of Nebraska⁻Lincoln, Lincoln, NE 68503, USA.
Department of Agronomy and Horticulture, University of Nebraska⁻Lincoln, Lincoln, NE 68503, USA.
Sensors (Basel). 2018 Nov 2;18(11):3731. doi: 10.3390/s18113731.
As one of the key crop traits, plant height is traditionally evaluated manually, which can be slow, laborious and prone to error. Rapid development of remote and proximal sensing technologies in recent years allows plant height to be estimated in more objective and efficient fashions, while research regarding direct comparisons between different height measurement methods seems to be lagging. In this study, a ground-based multi-sensor phenotyping system equipped with ultrasonic sensors and light detection and ranging (LiDAR) was developed. Canopy heights of 100 wheat plots were estimated five times during a season by the ground phenotyping system and an unmanned aircraft system (UAS), and the results were compared to manual measurements. Overall, LiDAR provided the best results, with a root-mean-square error (RMSE) of 0.05 m and an R² of 0.97. UAS obtained reasonable results with an RMSE of 0.09 m and an R² of 0.91. Ultrasonic sensors did not perform well due to our static measurement style. In conclusion, we suggest LiDAR and UAS are reliable alternative methods for wheat height evaluation.
作为作物的关键特性之一，植株高度传统上是通过人工进行评估的，这种方法耗时耗力，而且容易出错。近年来，远程和近程传感技术的快速发展使得能够更加客观、高效地估算植株高度，而关于不同高度测量方法之间的直接比较的研究似乎有些滞后。在这项研究中，开发了一种配备有超声波传感器和光探测和测距（LiDAR）的地面多传感器表型系统。该系统在一个季节内五次估算了 100 个小麦田块的冠层高度，并将结果与人工测量进行了比较。总体而言，LiDAR 的表现最好，其均方根误差（RMSE）为 0.05 米，R² 为 0.97。UAS 的 RMSE 为 0.09 米，R² 为 0.91，结果也较为合理。由于我们采用的是静态测量方式，超声波传感器的表现不佳。总之，我们建议 LiDAR 和 UAS 是可靠的小麦高度评估替代方法。
