• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >UAV-Borne Dual-Band Sensor Method for Monitoring Physiological Crop Status
【2h】

UAV-Borne Dual-Band Sensor Method for Monitoring Physiological Crop Status

机译：UAV-Borne双频传感器监测作物生理状态的方法

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Unmanned aerial vehicles (UAVs) equipped with dual-band crop-growth sensors can achieve high-throughput acquisition of crop-growth information. However, the downwash airflow field of the UAV disturbs the crop canopy during sensor measurements. To resolve this issue, we used computational fluid dynamics (CFD), numerical simulation, and three-dimensional airflow field testers to study the UAV-borne multispectral-sensor method for monitoring crop growth. The results show that when the flying height of the UAV is 1 m from the crop canopy, the generated airflow field on the surface of the crop canopy is elliptical, with a long semiaxis length of about 0.45 m and a short semiaxis of about 0.4 m. The flow-field distribution results, combined with the sensor’s field of view, indicated that the support length of the UAV-borne multispectral sensor should be 0.6 m. Wheat test results showed that the ratio vegetation index (RVI) output of the UAV-borne spectral sensor had a linear fit coefficient of determination (R2) of 0.81, and a root mean square error (RMSE) of 0.38 compared with the ASD Fieldspec2 spectrometer. Our method improves the accuracy and stability of measurement results of the UAV-borne dual-band crop-growth sensor. Rice test results showed that the RVI value measured by the UAV-borne multispectral sensor had good linearity with leaf nitrogen accumulation (LNA), leaf area index (LAI), and leaf dry weight (LDW); R2 was 0.62, 0.76, and 0.60, and RMSE was 2.28, 1.03, and 10.73, respectively. Our monitoring method could be well-applied to UAV-borne dual-band crop growth sensors.
机译：配备双波段农作物生长传感器的无人机可以实现农作物生长信息的高通量获取。然而，在传感器测量过程中，无人机的向下气流场干扰了作物的冠层。为解决此问题，我们使用计算流体力学（CFD），数值模拟和三维气流场测试仪来研究无人机载多光谱传感器方法来监测作物生长。结果表明，当无人机的飞行高度距作物冠层为1 m时，作物冠层表面产生的气流场为椭圆形，长半轴长约为0.45 m，短半轴长约为0.4 m。 。流场分布结果与传感器的视场相结合，表明无人机无人机多光谱传感器的支撑长度应为0.6 m。小麦测试结果表明，无人机传播的光谱传感器的比率植被指数（RVI）输出的线性拟合确定系数（R 2 ）为0.81，均方根误差（RMSE）与ASD Fieldspec2光谱仪相比为0.38。我们的方法提高了无人机载带双波段作物生长传感器的测量结果的准确性和稳定性。水稻试验结果表明，无人机载多光谱传感器测得的RVI值与叶氮累积量，叶面积指数和叶干重具有良好的线性关系。 R 2 分别为0.62、0.76和0.60，RMSE分别为2.28、1.03和10.73。我们的监测方法可以很好地应用于无人机携带的双波段农作物生长传感器。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号