Monitor Disease Incidence of Winter Wheat Stripe Rust Using Hyperspectral Data: Analysis of Characteristic Spectrum and Establishing of Inversion Models

Abstract:

It was discussed of the selection method of characteristic spectral band and the establishing of inversion model to monitor winter wheat stripe rust using hyperspectral data in this study. The correlation coefficients between the DI (disease incidence) at different stages of infection and the initial canopy reflectance spectral and the derivative of the reflectance spectrum were compared, respectively. The results showed that the derivative of the reflectance spectra has reached higher significant level with the DI than the initial reflectance spectral data. The initial reflectance in the visible light 680nm wavelength and the near infrared 976nm, 1010nm wavelength were selected to do regression with the DI of winter wheat stripe rust. And some inversion models between the DI and the hyperspectral data or its conversion patterns like NDVI (Normalized difference vegetation index), RVI (Ratio vegetation index), TVI (Transformed vegetation index) and its differential values of the canopy spectral reflectance data to monitor winter wheat stripe rust were established. Meanwhile, those correlation coefficients were compared respectively, of which we found the pattern of vegetation index has more efficient commonly than initial canopy spectral reflectance data by aggression analysis with the DI. The paper also suggested that the possibility of developing a special visible/near-infrared sensor for the detection of the DI of winter wheat stripe rust theoretically. Else, the SRSI (stripe rust stress index) mechanism model was presented for the first time in this paper.

• Periodical: Applied Mechanics and Materials (Volumes 602-605)
• Pages: 2462-2467
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.602-605.2462
• Online since: August 2014
• Authors: Mu Yi Huang*, Wen Jiang Huang, Xiao Dong Yang, Guang Zhou Chen
• Keywords: Characteristic Spectrum, Disease Incidence, Hyperspectral, Inversion Model, Winter Wheat Stripe Rust

References

[1] Li Guang-bo, Zeng Shi-mai, Li Zheng-qi. Integrated Management of Wheat Pests. Beijing: Press of Agriculture Science and Technology of China, 1989: 185-186.

[2] Gong Yanping, Huang Wenjiang, Wang Jihua. Progress and application of integrating GIS and RS technology to monitor and forecast crop diseases and pests [J]. Transactions of the CSAE, 2008, 24(Supp. 2): 331-336.

[3] Zhang Yuping, Guo Jiebin, Wang Shuang, Wang Haiguang, Ma Zhanhong. Relativity research on near-ground and satellite remote sensing reflectance of wheat stripe rust [J]. ACTA PHYTOPHYLACICA SINICA, 2009, 36(2): 119-122.

[4] Luo Juhua, Huang Wen-jiang, Gu Xiaohe, Ji Ning, Song Xiaoyu, Li Weiguo, Wei Zhaoling. Monitoring Stripe Rust of Winter Wheat Using PHI Based on Sensitive Bands [J]. Spectroscopy and Spectral Analysis, 2010, 30(1): 184-187.

[5] Zhang Jingcheng, Yuan Lin, Wang Jihua, Luo Juhua, Du Shizhou, Huang Wenjiang. Research progress of crop diseases and pests monitoring based on remote sensing [J]. Transactions of the Chinese Society of Agricultural Engineering. 2012. 20: 45-53.

[6] Zhang Xianzheng, Chen Fengyu etc. Experimental techniques of plant physiology. [M]. Shenyang: Liaoning Science and Technology Press: (1994).

[7] IV. S-65 The agriculture Compilation of China. [M]. Beijing: China Standard Press: (1998).

[8] Ruiliang Pu, Peng Gong. Hyperspectral Remote Sensing and Application. [M], Beijing: Higher education press, 2000: 185-202.

[9] Cloutis E. A, Hyperspectral geological remote sensing: evaluation of analytical techniques. [J] Int. J. Remote Sensing, 1996, 17 (12): 2215-2242.

DOI: 10.1080/01431169608948770