Using aerial imagery and digital photography to monitor growth and yield in winter wheat

被引:7
|
作者
Olanrewaju, Sarah [1 ,2 ]
Rajan, Nithya [1 ]
Ibrahim, Amir M. H. [1 ]
Rudd, Jackie C. [2 ]
Liu, Shuyu [2 ]
Sui, Ruixiu [3 ]
Jessup, Kirk E. [2 ]
Xue, Qingwu [2 ]
机构
[1] Texas A&M Univ, Dept Soil & Crop Sci, College Stn, TX 77843 USA
[2] Texas A&M AgriLife Res, Amarillo, TX 79119 USA
[3] USDA ARS, Stoneville, MS 38776 USA
基金
美国食品与农业研究所;
关键词
SPECTRAL REFLECTANCE INDEXES; DIFFERENCE WATER INDEX; VEGETATION INDEXES; GRAIN-YIELD; BREAD WHEAT; SELECTION; DROUGHT; BIOMASS; TRAITS;
D O I
10.1080/01431161.2019.1597303
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
Monitoring wheat (Triticum aestivum L.) performance throughout the growing season provides information on productivity and yield potential. Remote sensing tools have provided easy and quick measurements without destructive sampling. The objective of this study was to evaluate genetic variability in growth and performance of 20 wheat genotypes under two water regimes (rainfed and irrigated), using spectral vegetation indices (SVI) estimated from aerial imagery and percentage ground cover (%GC) estimated from digital photos. Field experiments were conducted at Bushland, Texas in two growing seasons (2014-2015 and 2015-2016). Digital photographs were taken using a digital camera in each plot, while a manned aircraft collected images of the entire field using a 12-band multiple camera array Tetracam system at three growth stages (tillering, jointing and heading). Results showed that a significant variation exists in SVI, %GC, aboveground biomass and yield among the wheat genotypes mostly at tillering and jointing. Significant relationships for %GC from digital photo at jointing was recorded with Normalized Difference Vegetation Index (NDVI) at tillering (coefficient of determination, R-2 = 0.84, p < 0.0001) and with %GC estimated from Perpendicular Vegetation Index (PVI) at tillering (R-2 = 0.83, p < 0.0001). Among the indices, Ratio Vegetation Index (RVI), Green-Red VI, Green Leaf Index (GLI), Generalized DVI (squared), DVI, Enhanced VI, Enhanced NDVI, and NDVI explained 37-99% of the variability in aboveground biomass and yield. Results indicate that these indices could be used as an indirect selection tool for screening a large number of early-generation and advanced wheat lines.
引用
收藏
页码:6905 / 6929
页数:25
相关论文
共 50 条
  • [1] Monitoring winter wheat growth at different heights using aerial imagery
    Miller, Jarrod O.
    Adkins, James
    AGRONOMY JOURNAL, 2021, 113 (02) : 1586 - 1595
  • [2] Evaluation of Aboveground Nitrogen Content of Winter Wheat Using Digital Imagery of Unmanned Aerial Vehicles
    Yang, Baohua
    Wang, Mengxuan
    Sha, Zhengxia
    Wang, Bing
    Chen, Jianlin
    Yao, Xia
    Cheng, Tao
    Cao, Weixing
    Zhu, Yan
    SENSORS, 2019, 19 (20)
  • [3] Using a Portable Active Sensor to Monitor Growth Parameters and Predict Grain Yield of Winter Wheat
    Zhang, Jiayi
    Liu, Xia
    Liang, Yan
    Cao, Qiang
    Tian, Yongchao
    Zhu, Yan
    Cao, Weixing
    Liu, Xiaojun
    SENSORS, 2019, 19 (05)
  • [4] USING AERIAL-PHOTOGRAPHY AND SATELLITE IMAGERY TO MONITOR FOREST COVER IN WESTERN SIBERIA
    SEDYKH, VN
    WATER AIR AND SOIL POLLUTION, 1995, 82 (1-2): : 499 - 507
  • [5] Digital archaeological aerial photography - Imagery processing and presentation formats
    Heller, HE
    ARCHAOLOGISCHES NACHRICHTENBLATT, 1998, 3 (01): : 6 - 12
  • [6] Detecting Wheat Powdery Mildew and Predicting Grain Yield Using Unmanned Aerial Photography
    Liu, Wei
    Cao, Xueren
    Fan, Jieru
    Wang, Zhenhua
    Yan, Zhengyuan
    Luo, Yong
    West, Jonathan S.
    Xu, Xiangming
    Zhou, Yilin
    PLANT DISEASE, 2018, 102 (10) : 1981 - 1988
  • [7] Rapid prediction of winter wheat yield and nitrogen use efficiency using consumer-grade unmanned aerial vehicles multispectral imagery
    Liu, Jikai
    Zhu, Yongji
    Tao, Xinyu
    Chen, Xiaofang
    Li, Xinwei
    FRONTIERS IN PLANT SCIENCE, 2022, 13
  • [8] PREDICTION OF THE YIELD OF WINTER-WHEAT USING DATA FROM AERIAL PHOTOGRAPHIC SURVEYS
    TRAKHOV, EM
    BULGAKOV, GS
    ISAULOVA, SS
    DZHAKUBOVA, TN
    PUGACHEV, AS
    ROMANENKO, TN
    SOVIET JOURNAL OF REMOTE SENSING, 1993, 10 (05): : 925 - 932
  • [9] Assessment of nitrogen status in wheat using aerial photography
    Zubillaga, M
    Urricariet, S
    COMMUNICATIONS IN SOIL SCIENCE AND PLANT ANALYSIS, 2005, 36 (13-14) : 1787 - 1798
  • [10] Integrating Early Growth Information to Monitor Winter Wheat Powdery Mildew Using Multi-Temporal Landsat-8 Imagery
    Ma, Huiqin
    Jing, Yuanshu
    Huang, Wenjiang
    Shi, Yue
    Dong, Yingying
    Zhang, Jingcheng
    Liu, Linyi
    SENSORS, 2018, 18 (10)