Monitoring of Wheat Scab Using the Specific Spectral Index from ASD Hyperspectral Dataset

被引:11
作者
Huang, Linsheng [1 ]
Zhang, Hansu [1 ,2 ]
Ding, Wenjuan [1 ]
Huang, Wenjiang [1 ,2 ]
Hu, Tingguang [1 ,2 ]
Zhao, Jinling [1 ]
机构
[1] Anhui Univ, Natl Engn Res Ctr Agroecol Big Data Anal & Applic, Hefei, Anhui, Peoples R China
[2] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing, Peoples R China
基金
中国国家自然科学基金;
关键词
VEGETATION INDEXES; WINTER-WHEAT; LEAF; LEAVES;
D O I
10.1155/2019/9153195
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
It is highly important to accurately monitor wheat scab and provide technical guidance for the crop pests and diseases. In this study, relevant analysis was performed among spectral reflectance, first-derivate data, and the disease severity data through ASD hyperspectral data. Two sensitive spectral wavelength ranges of 450-488 nm and 500-540 nm were selected. Then, a new wheat scab index (WSI) consisting of the two bands was proposed. The inversion models of the scab severities were comparatively built by unitary linear regression and multiple stepwise regression techniques. The results showed that the WSI had a significant linear relationship with severity of disease compared with other commonly used spectral indices. The fitting R-2, testing R-2, and RMSE were 0.73, 0.70, and 13.41, respectively. The multiple stepwise regression model established using the WSI, SDg/SDb, NBNDVI, and SDg as independent variables was better than the single-variable model. Our results suggest that WSI can be used to provide scientific guidance for monitoring and precise management of wheat scab disease.
引用
收藏
页数:9
相关论文
共 34 条
  • [1] Development of Spectral Disease Indices for 'Flavescence Doree' Grapevine Disease Identification
    AL-Saddik, Hania
    Simon, Jean-Claude
    Cointault, Frederic
    [J]. SENSORS, 2017, 17 (12)
  • [2] [Anonymous], 2018, JIANGSU AGR SCI
  • [3] Evaluating the Effect of Different Wheat Rust Disease Symptoms on Vegetation Indices Using Hyperspectral Measurements
    Ashourloo, Davoud
    Mobasheri, Mohammad Reza
    Huete, Alfredo
    [J]. REMOTE SENSING, 2014, 6 (06) : 5107 - 5123
  • [4] Red Edge Index as an Indicator of Vegetation Growth and Vigor Using Hyperspectral Remote Sensing Data
    Bandyopadhyay, Debmita
    Bhavsar, Dhruval
    Pandey, Kamal
    Gupta, Stutee
    Roy, Arijit
    [J]. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES INDIA SECTION A-PHYSICAL SCIENCES, 2017, 87 (04) : 879 - 888
  • [5] Early disease detection in wheat fields using spectral reflectance
    Bravo, C
    Moshou, D
    West, J
    McCartney, A
    Ramon, H
    [J]. BIOSYSTEMS ENGINEERING, 2003, 84 (02) : 137 - 145
  • [6] Comparing prediction power and stability of broadband and hyperspectral vegetation indices for estimation of green leaf area index and canopy chlorophyll density
    Broge, NH
    Leblanc, E
    [J]. REMOTE SENSING OF ENVIRONMENT, 2001, 76 (02) : 156 - 172
  • [7] Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance
    Daughtry, CST
    Walthall, CL
    Kim, MS
    de Colstoun, EB
    McMurtrey, JE
    [J]. REMOTE SENSING OF ENVIRONMENT, 2000, 74 (02) : 229 - 239
  • [8] Hyperspectral imaging for detection of scab in wheat
    Delwiche, SR
    Kim, MS
    [J]. BIOLOGICAL QUALITY AND PRECISION AGRICULTURE II, 2000, 4203 : 13 - 20
  • [9] Fang H, 2007, SPECTROSC SPECT ANAL, V27, P1731
  • [10] THE RED EDGE POSITION AND SHAPE AS INDICATORS OF PLANT CHLOROPHYLL CONTENT, BIOMASS AND HYDRIC STATUS
    FILELLA, I
    PENUELAS, J
    [J]. INTERNATIONAL JOURNAL OF REMOTE SENSING, 1994, 15 (07) : 1459 - 1470