Quantitative Identification of Yellow Rust and Powdery Mildew in Winter Wheat Based on Wavelet Feature
LU Jun-jing1, 2, HUANG Wen-jiang1*, ZHANG Jing-cheng3, JIANG Jin-bao2
1. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China 2. College of Resource Science and Technology, China University of Mine and Technology, Beijing 100083, China 3. Beijing Agriculture Information Technology Research Center, Beijing 100097, China
Abstract：Powdery mildew (Blumeria graminis) and stripe rust (Puccinia striiformis f. sp. Tritici) are two of the most prevalent and serious winter wheat diseases in the field, which caused heavy yield loss of winter wheat all over the world. It is necessary to quantitatively identify different diseases for spraying specific fungicides. This study examined the potential of quantitative distinction of powdery mildew and yellow rust by using hyperspectral data with continuous wavelet transform at canopy level. Spectral normalization was processing prior to other data analysis, given the differences of the groups in cultivars and soil environment. Then, continuous wavelet features were extracted from normalized spectral bands using continuous wavelet transform. Correlation analysis and independent t-test were used conjunctively to obtain sensitive spectral bands and continuous wavelet features of 350～1 300 nm, and then, principal component analysis was done to eliminate the redundancy of the spectral features. After that, Fisher linear discriminant models of powdery mildew, stripe rust and normal sample were built based on the principal components of SBs, WFs, and the combination of SBs & WFs, respectively. Finally, the methods of leave-one-out and 55 samples which have no share in model building were used to validate the models. The accuracies of classification were analyzed, it was indicated that the overall accuracies with 92.7% and 90.4% of the models based on WFs, were superior to those of SFs with 65.5% and 61.5%; However, the classification accuracies of Fisher 80-55 were higher but no different than leave-one-out cross validation model, which was possibly related to randomness of training samples selection. The overall accuracies with 94.6% and 91.1% of the models based on SBs & WFs were the highest; The producer’ accuracies of powdery mildew and healthy samples based on SBs & WFs were improved more than 10% than those of WFs in Fisher 80-55. Focusing on the discriminant accuracy of different disease, yellow rust can be discriminated in the model based on both WFs and SBs & WFs with higher accuracy; the user’ accuracy and producer’ accuracy were all up to 100%. The results show great potential of continuous wavelet features in discriminating different disease stresses, and provide theoretical basis for crop disease identification in wide range using remote sensing image.
Key words：Powdery mildew;Stripe rust;Spectral Bands;Wavelet Features;Fisher linear discrimination analysis
[1] Sankaran S, Mishra A, Ehsani R, et al. Computers and Electronics in Agriculture, 2010, 72(1): 1. [2] YUAN Lin, ZHANG Jing-cheng, ZHAO Jin-ling, et al(袁 琳, 张竞成, 赵晋陵, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2013, 33(6): 1608. [3] Guan Q S, Huang W J, Jiang J B, et al. Sensor Letters, 2014, 12: 1. [4] Huang W J, Guan Q S, Luo J H, et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 2516. [5] Zhang J C, Pu R L, Huang W J, et al. Field Crop Research, 2012b, 134: 165. [6] Zhang J C, Yuan L, Pu R L, et al. Computers and Electronics in Agriculture, 2014, 100: 80. [7] Cheng T, Rivard B, Sanchenz-Azofeifa G A. Remote Sensing of Environment, 2011, 115(2): 662. [8] Huang L S, Zhang D Y, Liang D, et al. International Journal of Agriculture & Biology, 2013, 15(1): 34. [9] WANG Ji-hua, ZHAO Chun-jiang, HUANG Wen-jiang(王纪华，赵春江，黄文江). Theory and Application of Advanced Remote Sensing in Agriculture(农业定量遥感基础与应用). Beijing： Science Press(北京： 科学出版社), 2008. [10] HUANG Wen-jiang(黄文江). Mechanism and Application of Crop Disease Monitoring Using Remote Sensing(作物病虫害遥感监测机理与应用). Beijing: China Agricultural Scientech Press(北京： 中国农业科学技术出版社)，2009. [11] Cheng T, Rivard B, Sanchenz-Azofeifa G A, et al. Remote Sensing of Environment, 2010, 114(4): 902. [12] Bruce L M, Li J. IEEE Transactions on Geoscience and Remote Sensing, 2001, 39(7): 1540. [13] Jolliffe I T. Principal Component Analysis, Springer, 2002. [14] ZHAO Ying-shi(赵英时). Analysis Principle and Applications of Remote Sensing(遥感应用分析原理与方法). Beijing: Science Press(北京： 科学出版社)，2003.
