| 光谱学与光谱分析 |
|
|
|
|
|
| Differentiation of Yellow Rust and Powdery Mildew in Winter Wheat and Retrieving of Disease Severity Based on Leaf Level Spectral Analysis |
| YUAN Lin1, 2, ZHANG Jing-cheng1, 2, ZHAO Jin-ling1, HUANG Wen-jiang3, WANG Ji-hua1, 2* |
1. Institute of Remote Sensing and Information Application, Zhejiang University, Hangzhou 310058, China
2. Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
3. Center for Earth Observation and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China |
|
|
|
|
Abstract Yellow rust and powdery mildew are two important diseases of winter wheat in China. The coincidence of their occurrence in field poses a challenge in their management and prevention. In the present study, the leaf spectra of the two diseases were measured by a spectrometer. Based on these data, we assessed the feasibility of differentiating the two diseases and evaluating their severity degrees. The disease sensitive bands and spectral features were identified through correlation analysis and independent t-test, including band regions at 665～684, 718～726 nm etc. and spectral features of DEP550-770, SIWSI etc. Based on these bands and spectral features, the models for disease discrimination and severity retrieving were developed according to FLDA and PLSR analysis, respectively. The results showed that the selected bands and spectral features can differentiate the yellow rust and powdery mildew explicitly, which yielded an OAA of 80%. It is noted that the discrimination model performed especially well (OAA=95%) in classifying those diseased leaves with damage proportion over 20%. The retrieving model of disease severity that were constructed by spectral features achieved reasonable estimates, with the RMSE for both diseases less than 15%. The leaf level models for discriminating powdery mildew and yellow rust and estimating their disease severity serve as a basis for further study in diseases differentiation and detection at canopy level.
|
|
Received: 2012-10-09
Accepted: 2013-02-16
|
|
|
|
Corresponding Authors:
WANG Ji-hua
E-mail: wangjh@nercita.org.cn
|
|
[1] Sankaran S, Mishra A, Ehsani R, et al. Computers and Electronics in Agriculture, 2010, 72(1): 1.
[2] ZHANG Jing-cheng, YUAN Lin, WANG Ji-hua, et al (张竞成，袁 琳，王纪华，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2012, 28(20): 1.
[3] Delwiche S R, Kim M S. Biological Quality and Precision Agriculture Ⅱ, 2000, 4203: 13.
[4] Liu Zhanyu, Wu Hongfeng, Huang Jing-feng. Computers and Electronics in Agriculture, 2010, 72(2): 99.
[5] LIU Liang-yun, HUANG Mu-yi, HUANG Wen-jiang, et al(刘良云，黄木易，黄文江, 等). Journal of Remote Sensing(遥感学报), 2004, 8(3): 275.
[6] Zhang Jingcheng, Pu Ruiliang, Wang Ji-hua, et al. Computers and Electronics in Agriculture, 2012a, 85: 13.
[7] Kuckenberg J, Tartachnyk I, Noga G. Biosystems Engineering, 2009, 103(2): 121.
[8] HUANG Mu-yi, HUANG Wen-jiang, LIU Liang-yun, et al(黄木易, 黄文江, 刘良云，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2004, 20(1): 176.
[9] Gong P, Pu R, Heald R C. International Journal of Remote Sensing, 2002, 23(9): 1827.
[10] Pu R, Foschi L, Gong P. International Journal of Remote Sensing, 2004, 25(20): 4267.
[11] Pu R, Ge S, Kelly N M, et al. International Journal of Remote Sensing, 2003, 24(9): 1799.
[12] Zhao C J, Huang M Y, Huang W J, et al. IGARSS, 2004, 6(20-24): 4318.
[13] Naidu R A, Perry E M, Pierce F J, et al. Computers and Electronics in Agriculture, 2009, 66(1): 38.
[14] Kim M S, Daughtry C S T, Chappelle E W, et al. In Proceedings of the 6th International Symposium on Physical Measurements and Signatures in Remote Sensing, France: Val d’Isere, 1994. 299.
[15] Merzlyak M N, Gitelson A A, Chivkunova O B, et al. Physiologia Plantarum, 1999, 106(1): 135.
[16] Gitelson A A, Merzlyak M N, Chivkunova O B. Photochemistry and Photobiology, 2001, 74(1): 38.
[17] Penuelas J, Pinol J, Ogaya R, et al. International Journal of Remote Sensing, 1997, 18: 2869.
[18] Fensholt R, Sandholt I. Remote Sensing of Environment, 2003, 87(1): 111.
[19] Nagler P L, Daughtry C S T, Goward S N. Remote Sensing of Environment, 2000, 71(2): 207.
[20] Zhuang J C, Pu R L, Huang W J, et al. Field Crop Research, 2012b, 134: 165.
[21] Devadas R, Lamb D W, Simpfendorfer S, et al. Precision Agriculture, 2009,10(6): 459. |
| [1] |
LIU Yan-de, WANG Shun. Research on Non-Destructive Testing of Navel Orange Shelf Life Imaging Based on Hyperspectral Image and Spectrum Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1792-1797. |
| [2] |
CHEN Yuan-zhe1, WANG Qiao-hua1, 2*, TIAN Wen-qiang1, XU Bu-yun1, HU Jian-chao1. Nondestructive Determinations of Texture and Quality of Preserved Egg Gel by Hyperspectral Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1985-1992. |
| [3] |
ZHANG Jie1, 2, XU Bo1, FENG Hai-kuan1, JING Xia2, WANG Jiao-jiao1, MING Shi-kang1, FU You-qiang3, SONG Xiao-yu1*. Monitoring Nitrogen Nutrition and Grain Protein Content of Rice Based on Ensemble Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1956-1964. |
| [4] |
ZHENG Yi1, 2, 3, WANG Yao1, 2, LIU Yan1, 2*. Study on Classification and Recognition of Mountain Meadow Vegetation Based on Seasonal Characteristics of Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1939-1947. |
| [5] |
PENG Ren-miao1, 2, XU Peng-peng2, ZHAO Yi-mo2, BAO Li-jun1, LI Cheng2*. Identification of Two-Dimensional Material Nanosheets Based on Deep Neural Network and Hyperspectral Microscopy Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1965-1973. |
| [6] |
JIANG Rong-chang1, 2, GU Ming-sheng2, ZHAO Qing-he1, LI Xin-ran1, SHEN Jing-xin1, 3, SU Zhong-bin1*. Identification of Pesticide Residue Types in Chinese Cabbage Based on Hyperspectral and Convolutional Neural Network[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1385-1392. |
| [7] |
JING Xia1, ZHANG Jie1, 2, WANG Jiao-jiao2, MING Shi-kang2, FU You-qiang3, FENG Hai-kuan2, SONG Xiao-yu2*. Comparison of Machine Learning Algorithms for Remote Sensing
Monitoring of Rice Yields[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1620-1627. |
| [8] |
JIANG Qing-hu1, LIU Feng1, YU Dong-yue2, 3, LUO Hui2, 3, LIANG Qiong3*, ZHANG Yan-jun3*. Rapid Measurement of the Pharmacological Active Constituents in Herba Epimedii Using Hyperspectral Analysis Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1445-1450. |
| [9] |
DAI Ruo-chen1, TANG Huan2*, TANG Bin1*, ZHAO Ming-fu1, DAI Li-yong1, ZHAO Ya3, LONG Zou-rong1, ZHONG Nian-bing1. Study on Detection Method of Foxing on Paper Artifacts Based on
Hyperspectral Imaging Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1567-1571. |
| [10] |
LI De-hui1, WU Tai-xia1*, WANG Shu-dong2*, LI Zhe-hua1, TIAN Yi-wei1, FEI Xiao-long1, LIU Yang1, LEI Yong3, LI Guang-hua3. Hyperspectral Indices for Identification of Red Pigments Used in Cultural Relic[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1588-1594. |
| [11] |
LIU Mei-chen, XUE He-ru*, LIU Jiang-ping, DAI Rong-rong, HU Peng-wei, HUANG Qing, JIANG Xin-hua. Hyperspectral Analysis of Milk Protein Content Using SVM Optimized by Sparrow Search Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1601-1606. |
| [12] |
LIU Yan-de, LI Mao-peng, HU Jun, XU Zhen, CUI Hui-zhen. Detection of Citrus Granulation Based on Near-Infrared
Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(05): 1366-1371. |
| [13] |
AN Ying1, 2, 4, DING Jing3, LIN Chao2, LIU Zhi-liang1, 4*. Inversion Method of Chlorophyll Concentration Based on
Relative Reflection Depths[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1083-1091. |
| [14] |
YU Yue, YU Hai-ye, LI Xiao-kai, WANG Hong-jian, LIU Shuang, ZHANG Lei, SUI Yuan-yuan*. Hyperspectral Inversion Model for SPAD of Rice Leaves Based on Optimized Spectral Index[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1092-1097. |
| [15] |
LI Meng1, 2, ZHANG Xiao-bo2, LIU Shao-bo3, CHEN Xing-feng4*, HUANG Lu-qi5*, SHI Ting-ting2, YANG Rui6, LIU Shu7, ZHENG Feng-jie8. Partly Interpretable Machine Learning Method of Ginseng Geographical Origins Recognition and Analysis by Hyperspectral Measurements[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(04): 1217-1221. |
|
|
|
|
