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
  • [31] EFFECTS OF NITROGEN APPLICATION LEVEL ON GROWTH AND YIELD OF WINTER WHEAT
    Liu, Jinbao
    Meng, Tingting
    Dong, Zhenyu
    Xie, Jiancang
    BANGLADESH JOURNAL OF BOTANY, 2022, 51 (04): : 937 - 941
  • [32] EAR GROWTH, DEVELOPMENTAL STAGES AND YIELD IN WINTER-WHEAT
    LEDENT, JF
    STOY, V
    AGRONOMIE, 1983, 3 (06): : 537 - 544
  • [33] Inversion of a canopy reflectance model using hyperspectral imagery for monitoring wheat growth and estimating yield
    Silke Migdall
    Heike Bach
    Jans Bobert
    Marc Wehrhan
    Wolfram Mauser
    Precision Agriculture, 2009, 10 : 508 - 524
  • [34] Inversion of a canopy reflectance model using hyperspectral imagery for monitoring wheat growth and estimating yield
    Migdall, Silke
    Bach, Heike
    Bobert, Jans
    Wehrhan, Marc
    Mauser, Wolfram
    PRECISION AGRICULTURE, 2009, 10 (06) : 508 - 524
  • [35] Low-altitude aerial photography for optimum N fertilization of winter wheat on the North China Plain
    Jia, L
    Buerkert, A
    Chen, X
    Roemheld, V
    Zhang, F
    FIELD CROPS RESEARCH, 2004, 89 (2-3) : 389 - 395
  • [36] Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (UAS)-Based Phenotyping
    Bhandari, Mahendra
    Baker, Shannon
    Rudd, Jackie C.
    Ibrahim, Amir M. H.
    Chang, Anjin
    Xue, Qingwu
    Jung, Jinha
    Landivar, Juan
    Auvermann, Brent
    REMOTE SENSING, 2021, 13 (06)
  • [37] Field-Scale Winter Wheat Growth Prediction Applying Machine Learning Methods with Unmanned Aerial Vehicle Imagery and Soil Properties
    Nduku, Lwandile
    Munghemezulu, Cilence
    Mashaba-Munghemezulu, Zinhle
    Masiza, Wonga
    Ratshiedana, Phathutshedzo Eugene
    Kalumba, Ahmed Mukalazi
    Chirima, Johannes George
    LAND, 2024, 13 (03)
  • [38] Quantitative Estimation of Wheat Phenotyping Traits Using Ground and Aerial Imagery
    Khan, Zohaib
    Chopin, Joshua
    Cai, Jinhai
    Eichi, Vahid-Rahimi
    Haefele, Stephan
    Miklavcic, Stanley J.
    REMOTE SENSING, 2018, 10 (06)
  • [39] Systematic Oblique Aerial Photography Using Multiple Digital Frame Cameras
    Petrie, Gordon
    PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 2009, 75 (02): : 102 - 107
  • [40] Quantifying geomembrane wrinkles using aerial photography and digital image processing
    Take, W. A.
    Chappel, M. J.
    Brachman, R. W. I.
    Rowe, R. K.
    GEOSYNTHETICS INTERNATIONAL, 2007, 14 (04) : 219 - 227