Using an unmanned aerial vehicle to evaluate nitrogen variability and height effect with an active crop canopy sensor

被引:0
|
作者
Brian Krienke
Richard B. Ferguson
Michael Schlemmer
Kyle Holland
David Marx
Kent Eskridge
机构
[1] University of Nebraska-Lincoln,
[2] Bayer Crop Science,undefined
[3] Holland Scientific Inc.,undefined
来源
Precision Agriculture | 2017年 / 18卷
关键词
Unmanned aerial vehicle (UAV); Active sensors; Imagery; Nitrogen variability; Maize;
D O I
暂无
中图分类号
学科分类号
摘要
Ground-based active sensors have been used in the past with success in detecting nitrogen (N) variability within maize production systems. The use of unmanned aerial vehicles (UAVs) presents an opportunity to evaluate N variability with unique advantages compared to ground-based systems. The objectives of this study were to: determine if a UAV was a suitable platform for use with an active crop canopy sensor to monitor in-season N status of maize, if UAV’s were a suitable platform, is the UAV and active sensor platform a suitable substitute for current handheld methods, and is there a height effect that may be confounding measurements of N status over crop canopies? In a 2013 study comparing aerial and ground-based sensor platforms, there was no difference in the ability of aerial and ground-based active sensors to detect N rate effects on a maize crop canopy. In a 2014 study, an active sensor mounted on a UAV was able to detect differences in crop canopy N status similarly to a handheld active sensor. The UAV/active sensor system (AerialActive) platform used in this study detected N rate differences in crop canopy N status within a range of 0.5–1.5 m above a relatively uniform turfgrass canopy. The height effect for an active sensor above a crop canopy is sensor- and crop-specific, which needs to be taken into account when implementing such a system. Unmanned aerial vehicles equipped with active crop canopy sensors provide potential for automated data collection to quantify crop stress in addition to passive sensors currently in use.
引用
收藏
页码:900 / 915
页数:15
相关论文
共 50 条
  • [21] The performance of a canopy relative height model (CRHM) in natural grassland aboveground biomass estimation using unmanned aerial vehicle data
    Yang, Yifeng
    Zhang, Mengjie
    Li, Jingsi
    Wang, Xu
    Yan, Yuchun
    Xin, Xiaoping
    Xu, Dawei
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2025, 233
  • [22] Time Series Field Estimation of Rice Canopy Height Using an Unmanned Aerial Vehicle-Based RGB/Multispectral Platform
    Li, Ziqiu
    Feng, Xiangqian
    Li, Juan
    Wang, Danying
    Hong, Weiyuan
    Qin, Jinhua
    Wang, Aidong
    Ma, Hengyu
    Yao, Qin
    Chen, Song
    AGRONOMY-BASEL, 2024, 14 (05):
  • [23] Evaluation of Soil Properties, Topographic Metrics, Plant Height, and Unmanned Aerial Vehicle Multispectral Imagery Using Machine Learning Methods to Estimate Canopy Nitrogen Weight in Corn
    Yu, Jody
    Wang, Jinfei
    Leblon, Brigitte
    REMOTE SENSING, 2021, 13 (16)
  • [24] Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models
    Yue, Jibo
    Yang, Guijun
    Li, Changchun
    Li, Zhenhai
    Wang, Yanjie
    Feng, Haikuan
    Xu, Bo
    REMOTE SENSING, 2017, 9 (07):
  • [25] Individual Tree Detection from Unmanned Aerial Vehicle (UAV) Derived Canopy Height Model in an Open Canopy Mixed Conifer Forest
    Mohan, Midhun
    Silva, Carlos Alberto
    Klauberg, Carine
    Jat, Prahlad
    Catts, Glenn
    Cardil, Adrian
    Hudak, Andrew Thomas
    Dia, Mahendra
    FORESTS, 2017, 8 (09):
  • [26] Influences of fractional vegetation cover on the spatial variability of canopy SIF from unmanned aerial vehicle observations
    Zhang, Xiaokang
    Zhang, Zhaoying
    Zhang, Yongguang
    Zhang, Qian
    Liu, Xinjie
    Chen, Jidai
    Wu, Yunfei
    Wu, Linsheng
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2022, 107
  • [27] Remote Measurement of Apple Orchard Canopy Information Using Unmanned Aerial Vehicle Photogrammetry
    Sun, Guoxiang
    Wang, Xiaochan
    Ding, Yongqian
    Lu, Wei
    Sun, Ye
    AGRONOMY-BASEL, 2019, 9 (11):
  • [28] Crop Segmentation of Unmanned Aerial Vehicle Imagery Using Edge Enhancement Network
    Li, Jinwen
    Pu, Fangling
    Chen, Hongjia
    Xu, Xin
    Yu, Yao
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2024, 21 : 1 - 5
  • [29] Estimation of Grapevine Crop Coefficient Using a Multispectral Camera on an Unmanned Aerial Vehicle
    Gautam, Deepak
    Ostendorf, Bertram
    Pagay, Vinay
    REMOTE SENSING, 2021, 13 (13)
  • [30] Editorial for the Special Issue "Estimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery"
    Jin, Xiuliang
    Li, Zhenhai
    Atzberger, Clement
    REMOTE SENSING, 2020, 12 (06)