Sequential application of hyperspectral indices for delineation of stripe rust infection and nitrogen deficiency in wheat

被引:35
作者
Devadas, R. [1 ,2 ]
Lamb, D. W. [2 ,3 ]
Backhouse, D. [4 ]
Simpfendorfer, S. [5 ]
机构
[1] Univ Technol Sydney, Fac Sci, Climate Change Cluster C3, Broadway, NSW 2007, Australia
[2] Cooperat Res Ctr Spatial Informat, Carlton, Vic 3053, Australia
[3] Univ New England, Precis Agr Res Grp, Armidale, NSW 2351, Australia
[4] Univ New England, Sch Environm & Rural Sci, Armidale, NSW 2351, Australia
[5] NSW Dept Primary Ind, Tamworth, NSW 2340, Australia
关键词
Wheat rust; Nitrogen; Vegetation index; Remote sensing; Hyperspectral; RADIATION-USE EFFICIENCY; SPECTRAL REFLECTANCE; VEGETATION INDEXES; WINTER-WHEAT; YELLOW RUST; DISEASE DETECTION; LEAVES; YIELD; IDENTIFICATION; SENESCENCE;
D O I
10.1007/s11119-015-9390-0
中图分类号
S [农业科学];
学科分类号
09 ;
摘要
Nitrogen (N) fertilization is crucial for the growth and development of wheat crops, and yet increased use of N can also result in increased stripe rust severity. Stripe rust infection and N deficiency both cause changes in foliar physiological activity and reduction in plant pigments that result in chlorosis. Furthermore, stripe rust produce pustules on the leaf surface which similar to chlorotic regions have a yellow color. Quantifying the severity of each factor is critical for adopting appropriate management practices. Eleven widely-used vegetation indices, based on mathematic combinations of narrow-band optical reflectance measurements in the visible/near infrared wavelength range were evaluated for their ability to discriminate and quantify stripe rust severity and N deficiency in a rust-susceptible wheat variety (H45) under varying conditions of nitrogen status. The physiological reflectance index (PhRI) and leaf and canopy chlorophyll index (LCCI) provided the strongest correlation with levels of rust infection and N-deficiency, respectively. When PhRI and LCCI were used in a sequence, both N deficiency and rust infection levels were correctly classified in 82.5 and 55 % of the plots at Zadoks growth stage 47 and 75, respectively. In misclassified plots, an overestimation of N deficiency was accompanied by an underestimation of the rust infection level or vice versa. In 18 % of the plots, there was a tendency to underestimate the severity of stripe rust infection even though the N-deficiency level was correctly predicted. The contrasting responses of the PhRI and LCCI to stripe rust infection and N deficiency, respectively, and the relative insensitivity of these indices to the other parameter makes their use in combination suitable for quantifying levels of stripe rust infection and N deficiency in wheat crops under field conditions.
引用
收藏
页码:477 / 491
页数:15
相关论文
共 46 条
  • [21] GOODING M.J., 1997, WHEAT PRODUCTION UTI
  • [22] New hyperspectral vegetation characterization parameters
    Gupta, RK
    Vijayan, D
    Prasad, TS
    [J]. CALIBRATION AND CHARACTERIZATION OF SATELLITE SENSORS AND ACCURACY OF DERIVED PHYSICAL PARAMETERS, 2001, 28 (01): : 201 - 206
  • [23] Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture
    Haboudane, D
    Miller, JR
    Tremblay, N
    Zarco-Tejada, PJ
    Dextraze, L
    [J]. REMOTE SENSING OF ENVIRONMENT, 2002, 81 (2-3) : 416 - 426
  • [24] Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression
    Hansen, PM
    Schjoerring, JK
    [J]. REMOTE SENSING OF ENVIRONMENT, 2003, 86 (04) : 542 - 553
  • [25] Hatfield PL, 1993, CROP PROT, V12, P403, DOI [10.1016/0261-2194(93)90001-Y, DOI 10.1016/0261-2194(93)90001-Y]
  • [26] Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging
    Huang, Wenjiang
    Lamb, David W.
    Niu, Zheng
    Zhang, Yongjiang
    Liu, Liangyun
    Wang, Jihua
    [J]. PRECISION AGRICULTURE, 2007, 8 (4-5) : 187 - 197
  • [27] Nitrogen-induced changes in colony density and spore production of Erysiphe graminis f sp hordei on seedlings of six spring barley cultivars
    Jensen, B
    Munk, L
    [J]. PLANT PATHOLOGY, 1997, 46 (02) : 191 - 202
  • [28] McRae F. J., 2008, WINTER CROP VARIETY
  • [29] Non-destructive optical detection of pigment changes during leaf senescence and fruit ripening
    Merzlyak, MN
    Gitelson, AA
    Chivkunova, OB
    Rakitin, VY
    [J]. PHYSIOLOGIA PLANTARUM, 1999, 106 (01) : 135 - 141
  • [30] Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps
    Moshou, D
    Bravo, C
    Oberti, R
    West, J
    Bodria, L
    McCartney, A
    Ramon, H
    [J]. REAL-TIME IMAGING, 2005, 11 (02) : 75 - 83