光谱学与光谱分析 |
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Early Diagnosis of Wheat Stripe Rust and Wheat Leaf Rust Using Near Infrared Spectroscopy |
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 |
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Abstract In the present study, near-infrared reflectance spectroscopy (NIRS) technology was applied to implement early diagnosis of two kinds of wheat rusts, i.e. wheat stripe rust and wheat leaf rust, by detecting wheat leaves as disease symptom has not appeared. The wheat leaves were divided into five categories including healthy leaves, leaves in the incubation period infected with P. striiformis f. sp. tritici, leaves showing symptom infected with P. striiformis f. sp. tritici, leaves in the incubation period infected with P. recondita f. sp. tritici and leaves showing symptom infected with P. recondita f. sp. tritici. Near infrared spectra of 150 wheat leaves were obtained using MPA spectrometer and then a model to identify the categories of wheat leaves was built using distinguished partial least squares (DPLS). For building the model, second-order derivative method was regarded as the best preprocessing method of the spectra and the spectral region 4 000~8 000 cm-1 was regarded as the optimal spectral region. Using the model with different training sets and testing sets, the average identification rate of the training sets was 96.56% and the average identification rate of the testing sets was 91.85%. The results proved the model’s stability. The optimal identification rates were obtained while the ratio of training set to testing set was 2∶1 and the number of principal components was 10. The identification rate of the training set was 97.00% and the identification rate of the testing set was 96.00%. The results indicated that the identification method based on the NIRS technology developed in this study is feasible for early diagnosis of wheat stripe rust and wheat leaf rust.
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Received: 2013-01-29
Accepted: 2013-04-22
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Corresponding Authors:
WANG Hai-guang
E-mail: wanghaiguang@cau.edu.cn
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[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] 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. [3] 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. [4] Winton L M, Stone J K, Watrud L S, et al. Phytopathology, 2002, 92: 112. [5] Zhao J, Wang X J, Chen C Q, et al. Plant Disease, 2007, 91: 1669. [6] Wang X J, Zheng W M, Buchenauer H, et al. European Journal of Plant Pathology, 2008, 120: 241. [7] 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. [8] LIU Fei, HE Yong, WANG Li(刘 飞, 何 勇, 王 莉). Acta Optica Sinica(光学学报), 2007, 27(11): 2054. [9] Chen Q S, Zhao J W, Liu M H, et al. Journal of Pharmaceutical and Biomedical Analysis, 2008, 46: 568. [10] Sankaran S, Mishra A, Ehsani R, et al. Computers and Electronics in Agriculture, 2010, 72: 1. [11] 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. [12] 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. [13] HUANG Mu-yi, WANG Ji-hua, HUANG Wen-jiang, et al(黄木易, 王纪华, 黄文江, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2003, 19(6): 154. [14] AN Hu, WANG Hai-guang, LIU Rong-ying, et al(安 虎, 王海光, 刘荣英, 等). China Plant Protection(中国植保导刊), 2005, 25(11): 8.
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