Discrimination of powdery mildew and yellow rust of winter wheat using high-resolution hyperspectra and imageries

Liang Dong; Liu Na; Zhang Dongyan; Zhao Jinling; Lin Fenfang; Huang Linsheng; Zhang Qing; Ding Yuwan

doi:10.3788/IRLA201746.0138004

Volume 46 Issue 1

Feb. 2017

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Liang Dong, Liu Na, Zhang Dongyan, Zhao Jinling, Lin Fenfang, Huang Linsheng, Zhang Qing, Ding Yuwan. Discrimination of powdery mildew and yellow rust of winter wheat using high-resolution hyperspectra and imageries[J]. Infrared and Laser Engineering, 2017, 46(1): 136004-0136004(9). doi: 10.3788/IRLA201746.0138004

Citation:

Liang Dong, Liu Na, Zhang Dongyan, Zhao Jinling, Lin Fenfang, Huang Linsheng, Zhang Qing, Ding Yuwan. Discrimination of powdery mildew and yellow rust of winter wheat using high-resolution hyperspectra and imageries[J]. Infrared and Laser Engineering, 2017, 46(1): 136004-0136004(9). doi: 10.3788/IRLA201746.0138004

Discrimination of powdery mildew and yellow rust of winter wheat using high-resolution hyperspectra and imageries

doi: 10.3788/IRLA201746.0138004

1.
Anhui Engineering Laboratory of Agro-Ecological Big Data,Anhui University,Hefei 230601,China;
2.
National Engineering Research Center for Information Technology in Agriculture,Beijing 100097,China;
3.
School of Geography and Remote Sensing,Nanjing University of Information Science & Technology,Nanjing 210044,China

Received Date: 2016-05-05
Rev Recd Date: 2016-06-03
Publish Date: 2017-01-25

Abstract

Disease stress is one of the main factors causing a reduction in wheat production and threatening food security. How to distinguish similar diseases accurately and diagnose disease severity scientifically is becoming a hot topic worldwide. The objective of this study is to discriminate powdery mildew and yellow rust of winter wheat, two common fungal diseases in the Chinese wheat-growing region. In the study, a high-resolution hyperspectral imaging system(ImSpector V10E) was utilized to capture spectral and imagery information of wheat leaves infected by two diseases. The dimensionality reduction of hyperspectral images was done by using principal component analysis(PCA), and with the density slice method, the recognition accuracy for the disease area at leaf level can be 97%. On this basis, the spectral difference of two diseases was analyzed, and 12 disease-sensitive bands were selected in the light of the second principal component(PC-2) images. The bands for powdery mildew were at 519, 643, 696, 764, 795 and 813 nm, while those for yellow rust were at 494, 630, 637, 698, 755 and 805 nm. Furthermore, a support vector machine(SVM) discriminant model was established based on selected sensitive wavebands, and its accuracy reached 92%. The results revealed that the hyperspectra combined with feature extraction of high-resolution imagery could effectively achieve discrimination of powdery mildew and yellow rust at leaf level, which will provide a theoretical foundation for developing a portable recognition device.
- hyperspectra,
- principal component analysis,
- support vector machine,
- density slice,
- disease discrimination

References

[1]	Chen Pengcheng, Zhang JianHua, Lei Yonghui. Research progress on hyperspectral remote sensing in monitoring crop diseases and insect pests[J]. Chinese Agricultural Science Bulletin, 2006, 22(3):388-391. (in Chinese)
[2]	Chen Bing, Li Shaokun, Wang Kerv. Studies of remote sensing on monitoring crop diseases and pests[J]. Cotton Science, 2007, 19(1):57-63. (in Chinese)
[3]	Luo Hongxia, Kan Yingbo, Wang Lingling. Hyperspectral remote sensing for crop diseases and pest dectection[J]. Guangdong Agricultural Sciences, 2012, 39(18):76-80. (in Chinese)
[4]	Zhang B H, Huang W, Li J B, et al. Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables:A review[J]. Food Research International, 2014, 62:326-343.
[5]	Hu Rongmeng, Wei Man. Research for extracting method of apple leaf ill spots based on hyperspectral image[J]. Journal of Northwest Agriculture and Forestry University(Natural Science Edition), 2012(8):95-99. (in Chinese)
[6]	Liu Liangyun, Huang Muyi, Huang Wenjiang. Monitoring stripe rust disease of winter wheat using muti-temporal hyperspectral airborne data[J]. Journal of Remote Sensing, 2004, 8(3):275-281.
[7]	Yang C M, Cheng C H. Spectral characteristics of rice plants infested by brown planthoppers[J]. Proceedings of the National Science Council Republic of China Part B:Life Sciences, 2001, 25(3):180-186.
[8]	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):1011-1105. (in Chinese)
[9]	Kuckenberg J, Kuckenberg J, Tartachnyk I, et al. Detection and differentiation of nitrogen-deficiency, powdery mildew and leaf rust at wheat leaf and canopy level by laser-induced chlorophyll fluorescence[J]. Biosystem Engineering, 2009, 103(2):121-128.
[10]	Feng Wei, Wang Xiaoyu, Song Xiao, et al. Estimation of severity level of wheat powdery mildew based on canopy spectral reflectance[J]. Acta Agronomica Sinica, 2013, 39(8):1469-1477. (in Chinese)
[11]	Huang Muyi, Wang Jihua, Huang Wenjiang. Hyperspectral character of stripe rust on winter wheat and monitoring by remote sensing[J]. Transactions of the CSAE, 2003, 19(6):154-158. (in Chinese)
[12]	Yuan Lin, Zhang Jingcheng, Zhao Jinling. Diffenentiation 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)
[13]	Huang Wenqian, Li Jiangbo, Zhang Chi. Identification of maize kernel embryo based on hyperspectral imaging technology and PCA[J]. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(s2):243-247. (in Chinese)
[14]	Li Jiangbo, Rao Xiuqin, Ying Yenbing. Advance on application of hyperspectral imaging to nondestructive detection of agricultural products external quality[J]. Spectroscopy and Spectral Analysis, 2011, 31(8):2021-2026. (in Chinese)
[15]	Ding L, Zhang Y P, Zhang X Y. Survey of image segmentation techniques and performance evaluation[J]. Computer Engineering and Software, 2010, 31(12):78-83.
[16]	Wu Dewen, Zhang Yuanfei. The best density separation method for extracting rock information from remote sensing image[J]. Remote Sensing for Land Resources, 2002, 14(4):51-54. (in Chinese)
[17]	Huang Changzhuan, Wang Bao, Yang Zhong. A Study on image segmentation techniques[J]. Computer Technology and Development, 2009, 19(6):76-79. (in Chinese)
[18]	Feng Xinyue, Yang Qiuxiang, An Yanyan, et al. Research of image denoising based on support vector machine and wavelet coefficients[J]. Microelectronics Computer, 2014(8):119-122. (in Chinese)
[19]	Zhang Jingcheng, Yuan Lin, Wang Jihua. 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)

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通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

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Discrimination of powdery mildew and yellow rust of winter wheat using high-resolution hyperspectra and imageries

1. Anhui Engineering Laboratory of Agro-Ecological Big Data,Anhui University,Hefei 230601,China;

2. National Engineering Research Center for Information Technology in Agriculture,Beijing 100097,China;

3. School of Geography and Remote Sensing,Nanjing University of Information Science & Technology,Nanjing 210044,China

Received Date: 2016-05-05

Rev Recd Date: 2016-06-03

Publish Date: 2017-01-25

Key words:

Abstract: Disease stress is one of the main factors causing a reduction in wheat production and threatening food security. How to distinguish similar diseases accurately and diagnose disease severity scientifically is becoming a hot topic worldwide. The objective of this study is to discriminate powdery mildew and yellow rust of winter wheat, two common fungal diseases in the Chinese wheat-growing region. In the study, a high-resolution hyperspectral imaging system(ImSpector V10E) was utilized to capture spectral and imagery information of wheat leaves infected by two diseases. The dimensionality reduction of hyperspectral images was done by using principal component analysis(PCA), and with the density slice method, the recognition accuracy for the disease area at leaf level can be 97%. On this basis, the spectral difference of two diseases was analyzed, and 12 disease-sensitive bands were selected in the light of the second principal component(PC-2) images. The bands for powdery mildew were at 519, 643, 696, 764, 795 and 813 nm, while those for yellow rust were at 494, 630, 637, 698, 755 and 805 nm. Furthermore, a support vector machine(SVM) discriminant model was established based on selected sensitive wavebands, and its accuracy reached 92%. The results revealed that the hyperspectra combined with feature extraction of high-resolution imagery could effectively achieve discrimination of powdery mildew and yellow rust at leaf level, which will provide a theoretical foundation for developing a portable recognition device.

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Reference (19)

[1]	Chen Pengcheng, Zhang JianHua, Lei Yonghui. Research progress on hyperspectral remote sensing in monitoring crop diseases and insect pests[J]. Chinese Agricultural Science Bulletin, 2006, 22(3):388-391. (in Chinese)
[2]	Chen Bing, Li Shaokun, Wang Kerv. Studies of remote sensing on monitoring crop diseases and pests[J]. Cotton Science, 2007, 19(1):57-63. (in Chinese)
[3]	Luo Hongxia, Kan Yingbo, Wang Lingling. Hyperspectral remote sensing for crop diseases and pest dectection[J]. Guangdong Agricultural Sciences, 2012, 39(18):76-80. (in Chinese)
[4]	Zhang B H, Huang W, Li J B, et al. Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables:A review[J]. Food Research International, 2014, 62:326-343.
[5]	Hu Rongmeng, Wei Man. Research for extracting method of apple leaf ill spots based on hyperspectral image[J]. Journal of Northwest Agriculture and Forestry University(Natural Science Edition), 2012(8):95-99. (in Chinese)
[6]	Liu Liangyun, Huang Muyi, Huang Wenjiang. Monitoring stripe rust disease of winter wheat using muti-temporal hyperspectral airborne data[J]. Journal of Remote Sensing, 2004, 8(3):275-281.
[7]	Yang C M, Cheng C H. Spectral characteristics of rice plants infested by brown planthoppers[J]. Proceedings of the National Science Council Republic of China Part B:Life Sciences, 2001, 25(3):180-186.
[8]	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):1011-1105. (in Chinese)
[9]	Kuckenberg J, Kuckenberg J, Tartachnyk I, et al. Detection and differentiation of nitrogen-deficiency, powdery mildew and leaf rust at wheat leaf and canopy level by laser-induced chlorophyll fluorescence[J]. Biosystem Engineering, 2009, 103(2):121-128.
[10]	Feng Wei, Wang Xiaoyu, Song Xiao, et al. Estimation of severity level of wheat powdery mildew based on canopy spectral reflectance[J]. Acta Agronomica Sinica, 2013, 39(8):1469-1477. (in Chinese)
[11]	Huang Muyi, Wang Jihua, Huang Wenjiang. Hyperspectral character of stripe rust on winter wheat and monitoring by remote sensing[J]. Transactions of the CSAE, 2003, 19(6):154-158. (in Chinese)
[12]	Yuan Lin, Zhang Jingcheng, Zhao Jinling. Diffenentiation 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)
[13]	Huang Wenqian, Li Jiangbo, Zhang Chi. Identification of maize kernel embryo based on hyperspectral imaging technology and PCA[J]. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(s2):243-247. (in Chinese)
[14]	Li Jiangbo, Rao Xiuqin, Ying Yenbing. Advance on application of hyperspectral imaging to nondestructive detection of agricultural products external quality[J]. Spectroscopy and Spectral Analysis, 2011, 31(8):2021-2026. (in Chinese)
[15]	Ding L, Zhang Y P, Zhang X Y. Survey of image segmentation techniques and performance evaluation[J]. Computer Engineering and Software, 2010, 31(12):78-83.
[16]	Wu Dewen, Zhang Yuanfei. The best density separation method for extracting rock information from remote sensing image[J]. Remote Sensing for Land Resources, 2002, 14(4):51-54. (in Chinese)
[17]	Huang Changzhuan, Wang Bao, Yang Zhong. A Study on image segmentation techniques[J]. Computer Technology and Development, 2009, 19(6):76-79. (in Chinese)
[18]	Feng Xinyue, Yang Qiuxiang, An Yanyan, et al. Research of image denoising based on support vector machine and wavelet coefficients[J]. Microelectronics Computer, 2014(8):119-122. (in Chinese)
[19]	Zhang Jingcheng, Yuan Lin, Wang Jihua. 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)

Discrimination of powdery mildew and yellow rust of winter wheat using high-resolution hyperspectra and imageries

doi: 10.3788/IRLA201746.0138004

Abstract

References

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通讯作者: 陈斌, bchen63@163.com

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