光谱学与光谱分析 |
|
|
|
|
|
Detection of Puccinia striiformis f. sp. tritici Latent Infections in Wheat Leaves Using Near Infrared Spectroscopy Technology |
LI Xiao-long1, MA Zhan-hong1, ZHAO Long-lian2, LI Jun-hui2, WANG Hai-guang1* |
1. College of Agriculture and Biotechnology, China Agricultural University, Beijing 100193, China 2. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China |
|
|
Abstract To realize the early detection of P. striiformis f. sp. tritici latent infections in wheat leaves while no disease symptoms appear, a qualitative model for identification of the wheat leaves in the incubation period of stripe rust was built using near infrared reflectance spectroscopy (NIRS) technology combined with qualitative partial least squares (DPLS). In this study, 30 leaf samples infected with P. striiformis f. sp. tritici were collected each day during the eleven-day incubation period. And 30 healthy leaf samples and 30 leaf samples showing disease symptoms infected with P. striiformis f. sp. tritici, were also collected as controls. In total, there were 390 leaf samples that were divided into thirteen categories. Near infrared spectra of 390 leaf samples were obtained using MPA spectrometer and then a model to identify the categories of wheat leaves was built using cross verification method. The effects of different spectral ranges, samples for building the model, preprocessing methods of spectra and number of principal components on NIRS prediction results for qualitative identification were investigated. The optimal identification results were obtained for the model built in the combined spectral region of 5 400~6 600 and 7 600~8 900 cm-1 when the spectra were divided into the training set and the testing set with the ratio equal to 4∶1, “scatter correction” was used as the preprocessing method and the number of principal components was 14. Accuracy rate, misjudgment rate and confusion rate of the training set were 95.51%, 1.28% and 3.21%, respectively. And accuracy rate, misjudgment rate and confusion rate of the testing set were 100.00%, 0.00% and 0.00%, respectively. The result showed that using near infrared reflectance spectroscopy technology, P. striiformis f. sp. tritici latent infections in wheat leaves could be detected as early as one day after inoculation (or 11 days before symptoms appearing) and the number of days when the leaf has been infected could also be identified. The results indicated that the method using near infrared reflectance spectroscopy technology proposed in this study is feasible for the identification of wheat leaves latently infected by P. striiformis f. sp. tritici. A new method based on NIRS was provided for the early detection of wheat stripe rust in this study.
|
Received: 2013-08-19
Accepted: 2013-12-22
|
|
Corresponding Authors:
WANG Hai-guang
E-mail: wanghaiguang@cau.edu.cn
|
|
[1] LI Zhen-qi, ZENG Shi-mai(李振岐, 曾士迈). Wheat Rusts in China(中国小麦锈病). Beijing: China Agriculture Press(北京: 中国农业出版社), 2002. 1. [2] Wan A, Zhao Z, Chen X, et al. Plant Disease, 2004, 88(8): 896. [3] Wan A M, Chen X M, He Z H. Australian Journal of Agricultural Research, 2007, 58(6): 605. [4] CAI Cheng-jing, WANG Hai-guang, AN Hu, et al(蔡成静, 王海光, 安 虎, 等). Journal of Northwest Sci-Tech Univ. of Agri. and For.·Nat. Sci. Ed.(西北农林科技大学学报·自然科学版), 2005, 33(Supp.): 31. [5] LI Jing, CHEN Yun-hao, JIANG Jin-bao, et al(李 京, 陈云浩, 蒋金豹, 等). Science & Technology Review(科技导报), 2007, 25(6): 23. [6] Winton L M, Stone J K, Watrud L S, et al. Phytopathology, 2002, 92(1): 112. [7] Zhao J, Wang X J, Chen C Q, et al. Plant Disease, 2007, 91(12): 1669. [8] Wang X J, Zheng W M, Buchenauer H, et al. European Journal of Plant Pathology, 2008, 120(3): 241. [9] Wang X J, Tang C L, Chen J L, et al. Journal of Phytopathology, 2009, 157(7-8): 490. [10] PAN Juan-juan, LUO Yong, HUANG Chong, et al(潘娟娟, 骆 勇, 黄 冲, 等). Acta Phytopathologica Sinica(植物病理学报), 2010, 40(5): 504. [11] YAN Yan-lu, ZHAO Long-lian, HAN Dong-hai, et al(严衍禄, 赵龙莲, 韩东海, 等). Basis and Application of Near Infrared Reflectance Spectroscopy(近红外光谱分析基础与应用). Beijing: China Light Industry Press(北京: 中国轻工业出版社), 2005. 2. [12] Chen Q S, Zhao J W, Liu M H, et al. Journal of Pharmaceutical and Biomedical Analysis, 2008, 46(3): 568. [13] Alamprese C, Casale M, Sinelli N, et al. LWT-Food Science and Technology, 2013, 53(1): 225. [14] Sankaran S, Mishra A, Ehsani R, et al. Computers and Electronics in Agriculture, 2010, 72(1): 1. [15] WU Di, FENG Lei, ZHANG Chuan-qing, et al(吴 迪, 冯 雷, 张传清, 等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2007, 26(4): 269. [16] FENG Lei, CHEN Shuang-shuang, FENG Bin, et al(冯 雷, 陈双双, 冯 斌, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2012, 28(1): 139. [17] Gachon C, Saindrenan P. Plant Physiology and Biochemistry, 2004, 42(5): 367. [18] Mercado-Blanco J, Collado-Romero M, Parrilla-Araujo S, et al. Physiological and Molecular Plant Pathology, 2003, 63(2): 91. |
[1] |
GAO Feng1, 2, XING Ya-ge3, 4, LUO Hua-ping1, 2, ZHANG Yuan-hua3, 4, GUO Ling3, 4*. Nondestructive Identification of Apricot Varieties Based on Visible/Near Infrared Spectroscopy and Chemometrics Methods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 44-51. |
[2] |
BAO Hao1, 2,ZHANG Yan1, 2*. Research on Spectral Feature Band Selection Model Based on Improved Harris Hawk Optimization Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 148-157. |
[3] |
HU Cai-ping1, HE Cheng-yu2, KONG Li-wei3, ZHU You-you3*, WU Bin4, ZHOU Hao-xiang3, SUN Jun2. Identification of Tea Based on Near-Infrared Spectra and Fuzzy Linear Discriminant QR Analysis[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3802-3805. |
[4] |
LIU Xin-peng1, SUN Xiang-hong2, QIN Yu-hua1*, ZHANG Min1, GONG Hui-li3. Research on t-SNE Similarity Measurement Method Based on Wasserstein Divergence[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3806-3812. |
[5] |
BAI Xue-bing1, 2, SONG Chang-ze1, ZHANG Qian-wei1, DAI Bin-xiu1, JIN Guo-jie1, 2, LIU Wen-zheng1, TAO Yong-sheng1, 2*. Rapid and Nndestructive Dagnosis Mthod for Posphate Dficiency in “Cabernet Sauvignon” Gape Laves by Vis/NIR Sectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3719-3725. |
[6] |
WANG Qi-biao1, HE Yu-kai1, LUO Yu-shi1, WANG Shu-jun1, XIE Bo2, DENG Chao2*, LIU Yong3, TUO Xian-guo3. Study on Analysis Method of Distiller's Grains Acidity Based on
Convolutional Neural Network and Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3726-3731. |
[7] |
LUO Li, WANG Jing-yi, XU Zhao-jun, NA Bin*. Geographic Origin Discrimination of Wood Using NIR Spectroscopy
Combined With Machine Learning Techniques[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3372-3379. |
[8] |
ZHANG Shu-fang1, LEI Lei2, LEI Shun-xin2, TAN Xue-cai1, LIU Shao-gang1, YAN Jun1*. Traceability of Geographical Origin of Jasmine Based on Near
Infrared Diffuse Reflectance Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3389-3395. |
[9] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
[10] |
HUANG Meng-qiang1, KUANG Wen-jian2, 3*, LIU Xiang1, HE Liang4. Quantitative Analysis of Cotton/Polyester/Wool Blended Fiber Content by Near-Infrared Spectroscopy Based on 1D-CNN[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3565-3570. |
[11] |
HUANG Zhao-di1, CHEN Zai-liang2, WANG Chen3, TIAN Peng2, ZHANG Hai-liang2, XIE Chao-yong2*, LIU Xue-mei4*. Comparing Different Multivariate Calibration Methods Analyses for Measurement of Soil Properties Using Visible and Short Wave-Near
Infrared Spectroscopy Combined With Machine Learning Algorithms[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3535-3540. |
[12] |
KANG Ming-yue1, 3, WANG Cheng1, SUN Hong-yan3, LI Zuo-lin2, LUO Bin1*. Research on Internal Quality Detection Method of Cherry Tomatoes Based on Improved WOA-LSSVM[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3541-3550. |
[13] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
[14] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
[15] |
GUO Ge1, 3, 4, ZHANG Meng-ling3, 4, GONG Zhi-jie3, 4, ZHANG Shi-zhuang3, 4, WANG Xiao-yu2, 5, 6*, ZHOU Zhong-hua1*, YANG Yu2, 5, 6, XIE Guang-hui3, 4. Construction of Biomass Ash Content Model Based on Near-Infrared
Spectroscopy and Complex Sample Set Partitioning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3143-3149. |
|
|
|
|