Unmanned aerial vehicle remote sensing to delineate cotton root rot

被引:8
|
作者
Wang, Tianyi [1 ]
Thomasson, J. Alex [1 ,2 ]
Yang, Chenghai [3 ]
Isakeit, Thomas [4 ]
Nichols, Robert L. [5 ]
Collett, Ryan M. [6 ]
Han, Xiongzhe [1 ,7 ]
Bagnall, Cody [1 ]
机构
[1] Texas A&M Univ, Dept Biol & Agr Engn, College Stn, TX 77843 USA
[2] Mississippi State Univ, Dept Agr & Biol Engn, Starkville, MS USA
[3] USDA ARS, Aerial Applicat Technol Res Unit, College Stn, TX USA
[4] Texas A&M Univ, Dept Plant Pathol & Microbiol, College Stn, TX 77843 USA
[5] Cotton Inc, Agr & Environm Res, Cary, NC USA
[6] Texas A&M Agrilife Extens, Stiles Farm, Thrall, TX USA
[7] Kangwon Natl Univ, Coll Agr & Life Sci, Dept Biosyst Engn, Chunchon, Kangwon, South Korea
来源
JOURNAL OF APPLIED REMOTE SENSING | 2020年 / 14卷 / 03期
关键词
remote sensing; unmanned aerial vehicle; cotton root rot; classification; machine learning; prescription map;
D O I
10.1117/1.JRS.14.034522
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Cotton root rot (CRR) is a persistent soil-borne fungal disease that is devastating to cotton crops in certain fields, predominantly in Texas. Research has shown that CRR can be prevented or mitigated by applying fungicide during planting, but fungicide application is expensive. The potentially infected area within a field has been shown to be consistent, so it is possible to apply the fungicide only at locations where CRR exists, thus minimizing the amount of fungicide applied across the field. Previous studies have shown that remote sensing from manned aircraft is an effective means of delineating CRR-infected field areas. In 2015, an unmanned aerial vehicle was used to collect high-resolution remote-sensing images in a field known to be infected with CRR. A method was developed to produce a prescription map (PM) from these data, and in 2017, fungicide was applied based on a PM derived from the 2015 image data. The results showed that the PM reduced the fungicide applied by 88.3%, with a reduction in CRR area of 90% compared to 2015. A simple economic model suggested that it is generally better to treat an entire CRR-infested field rather than leaving it untreated, and application based on a PM becomes preferable as the size of the farm and the yield increase while the CRR-infestation level and the number of fields on the farm decrease. (C) 2020 Society of Photo-Optical Instrumentation Engineers (SPIE)
引用
收藏
页数:13
相关论文
共 50 条
  • [31] A NEW TECHNOLOGY FOR RAILWAY ENGINEERING SURVEYING AND INVESTIGATION: UNMANNED AERIAL VEHICLE REMOTE SENSING
    Wu, Wenbin
    Tan, Qulin
    Hu, Jiping
    NEW TECHNOLOGIES OF RAILWAY ENGINEERING, 2012, : 349 - 352
  • [32] Area extraction of maize lodging based on remote sensing by small unmanned aerial vehicle
    Chen, Zhongxin, 1600, Chinese Society of Agricultural Engineering (30):
  • [33] UNMANNED AERIAL VEHICLE (UAV) BASED REMOTE SENSING FOR CROP PATTERN MAPPING, TURKEY
    Sener, Mehmet
    Pehlivan, Mevlut
    Tekiner, Murat
    Alkan, Cayan
    Ozden, U. Evrim
    Erdem, Tolga
    Celen, H. Huseyin
    Seren, Ahmet
    Aytac, S. Aydin
    Kolsuz, H. Ugur
    Seyrek, Kemal
    Guresci, Gulsah
    Kose, Gokhan
    Turan, Lokman
    FRESENIUS ENVIRONMENTAL BULLETIN, 2018, 27 (12A): : 8831 - 8837
  • [34] Methods and datasets on semantic segmentation for Unmanned Aerial Vehicle remote sensing images: A review
    Cheng, Jian
    Deng, Changjian
    Su, Yanzhou
    An, Zeyu
    Wang, Qi
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2024, 211 : 1 - 34
  • [35] Boost Precision Agriculture with Unmanned Aerial Vehicle Remote Sensing and Edge Intelligence: A Survey
    Liu, Jia
    Xiang, Jianjian
    Jin, Yongjun
    Liu, Renhua
    Yan, Jining
    Wang, Lizhe
    REMOTE SENSING, 2021, 13 (21)
  • [36] Cotton row spacing and unmanned aerial vehicle sensors
    Wu, Wenzhuo
    Hague, Steve Scott
    Jung, Jinha
    Ashapure, Akash
    Maeda, Murilo
    Maeda, Andrea
    Chang, Anjin
    Jones, Don
    Thomasson, Alex
    Landivar, Juan
    AGRONOMY JOURNAL, 2022, 114 (01) : 331 - 339
  • [37] Plant-by-Plant Level Classification of Cotton Root Rot by UAV Remote Sensing
    Wang, T.
    Thomasson, J. A.
    AUTONOMOUS AIR AND GROUND SENSING SYSTEMS FOR AGRICULTURAL OPTIMIZATION AND PHENOTYPING IV, 2019, 11008
  • [38] Method for automatic georeferencing aerial remote sensing (RS) images from an unmanned aerial vehicle (UAV) platform
    Xiang, Haitao
    Tian, Lei
    BIOSYSTEMS ENGINEERING, 2011, 108 (02) : 104 - 113
  • [39] Control Strategy of Frequent Overflow at Intersection Based on Remote Sensing of Unmanned Aerial Vehicle and Vehicle Trajectory Data
    Zhang, Lili
    Wang, Xinzhe
    Su, Lichen
    Wang, Fang
    Wei, Wei
    Xu, Jiamei
    Li, Jing
    Yu, Pei
    Zhao, Qi
    JOURNAL OF SENSORS, 2022, 2022
  • [40] Vegetation Classification of Desert Steppe Based on Unmanned Aerial Vehicle Remote Sensing and Random Forest
    Yang H.
    Du J.
    Ruan P.
    Zhu X.
    Liu H.
    Wang Y.
    Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery, 2021, 52 (06): : 186 - 194