Early diagnosis of wheat powdery mildew based on Relief-F band screening

Huang Linsheng; Zhang Qing; Zhang Dongyan; Lin Fenfang; Xu Chao; Zhao Jinling

doi:10.3788/irla201847.0523001

[1] Zhang Jingcheng, Yuan Lin, Wang Jihua, et al. Research progress of crop diseases and pests monitoring based on remote sensing [J]. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(20): 1-11. (in Chinese)

[2] Qiao Hongbo, Zhou Yinlin, Bai Youlu, et al. The primary research of detecting wheat powdery mildew using in-field and low altitude remote sensing[J]. Acta Phytophylacica Sinica, 2006, 33(4): 341-344. (in Chinese)

[3] Yuan Lin, Zhang Jingcheng, Zhao Jinling, et al. Differentiation of yellow rust and powdery mildew in winter wheat and retrieving of disease severity based on leaf level spectral analysis[J]. Spectroscopy and Spectral Analysis, 2013, 33(6): 1608-1614. (in Chinese)

[4] Zhang Jingcheng, Pu Ruiliang, Wang Jihua, et al. Detecting powdery mildew of winter wheat using leaf level hyperspectral measurements[J]. Computers & Electronics in Agriculture, 2012, 85(1):13-23.

[5] Liang Dong, Liu Na, Zhang Dongyan, et al. Discrimination of powdery mildew and yellow rust of winter wheat using high-resolution hyperspectra and imageries[J]. Infrared and Laser Engineering, 2017, 46(1): 0138004. (in Chinese)

[6] Newton A C, Hackett C A, Lowe R, et al. Relationship between canopy reflectance and yield loss due to disease in barley[J]. Annals of Applied Biology, 2015, 145(1):95-106.

[7] Mahlein A K, Rumpf T, Welke P, et al. Development of spectral indices for detecting and identifying plant diseases[J]. Remote Sensing of Environment, 2013, 1(128): 21-30.

[8] Huang Linsheng, Zhang Dongyan, Liang Dong, et al. Continuous wavelet analysis for diagnosing stress characteristics of leaf powdery mildew. [J]. International Journal of Agriculture & Biology, 2013, 15(1): 34-40.

[9] Liang Dong, Yang Qinying, Huang Wenjiang, et al. Estimation of leaf area index based on wavelet transform and support vector machine regression in winter wheat[J]. Infrared and Laser Engineering, 2015, 44(1): 335-340. (in Chinese)

[10] Liang Liang, Yang Minhua, Zhang Lianpeng, et al. Chlorophyll content inversion with hyperspectral technology for wheat canopy based on support vector regression algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(20): 162-171. (in Chinese)

[11] Ding Shifei, Qi Bingjuan, Tan Hongyan. An overview on theory and algorithm of support vector machines[J]. Journal of University of Electronic Science and Technology of China, 2011, 40(1): 2-10. (in Chinese)

[12] Zhang Dongyan, Zhang Jingcheng, Zhu Dazhou, et al. Investigation of the hyperspectral image characteristics of wheat leaves under different stress[J]. Spectroscopy and Spectral Analysis, 2011, 31(4): 1101-1105. (in Chinese)

[13] Huang Wenjiang, Guan Qingsong, Luo Juhua, et al. New optimized spectral indices for identifying and monitoring winter wheat diseases[J]. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 2014, 7(6): 2516-2524.

[14] Kononenko I. Estimating attributes: analysis and extensions of RELIEF[C]//European Conference on Machine Learning on Machine Learning, 1994: 171-182.

[15] Zhang Difei, Zhang Jinsuo, Yao Keming, et al. Infrared ship-target recognition based on SVM classification[J]. Infrared and Laser Engineering, 2016, 45(1): 0104004. (in Chinese)

[16] Liang Dong, Xie Qiaoyun, Huang Wenjiang, et al. Using least squares support vector machines to estimate time series leaf area index[J]. Infrared and Laser Engineering, 2014, 43(1): 243-248. (in Chinese)