Identification and Classification of Disease Severity of Wheat Stripe Rust Using Near Infrared Spectroscopy Technology
LI Xiao-long1, QIN Feng1, ZHAO Long-lian2, LI Jun-hui2, MA Zhan-hong1, 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：Wheat stripe rust caused by Puccinia striiformis f. sp. tritici, is an economically important disease in the world. It is of great significance to assess disease severity of wheat stripe rust quickly and accurately for monitoring and controlling the disease. In this study, wheat leaves infected with stripe rust pathogen under different severity levels were acquired through artificial inoculation in artificial climate chamber. Thirty wheat leaves with disease severity equal to 1%, 5%, 10%, 20%, 40%, 60%, 80% or 100% were picked out, respectively, and 30 healthy leaves were chosen as controls. A total of 270 wheat leaves were obtained and then their near infrared spectra were measured using MPA spectrometer. According to disease severity levels, 270 near infrared spectra were divided into 9 categories and each category included 30 spectra. From each category, 7 or 8 spectra were randomly chosen to make up the testing set that included 67 spectra. The remaining spectra were treated as the training set. A qualitative model for identification and classification of disease severity of wheat stripe rust was built using near infrared reflectance spectroscopy (NIRS) technology combined with discriminant partial least squares (DPLS). The effects of different preprocessing methods of obtained spectra, ratios between training sets and testing sets, and spectral ranges on qualitative recognition results of the model were investigated. The optimal model based on DPLS was built using cross verification method in the spectral region of 4 000～9 000 cm-1 when “centralization” was used as the preprocessing method of spectra and the spectra were divided into the training set and the testing set with the ratio equal to 3∶1. Accuracy rate of the training set was 95.57% and accuracy rate of the testing set was 97.01%. The results showed that good recognition performance could be acquired using the model based on DPLS. The results indicated that the method using near infrared reflectance spectroscopy technology proposed in this study is feasible for identification and classification of disease severity of wheat stripe rust. A new method was provided for monitoring and assessment of wheat stripe rust.
Key words：Near infrared spectroscopy;Wheat stripe rust;Disease severity;Classification and identification
李小龙1，秦 丰1，赵龙莲2，李军会2，马占鸿1，王海光1* . 近红外光谱技术的小麦条锈病严重度分级识别 [J]. 光谱学与光谱分析, 2015, 35(02): 367-371.
LI Xiao-long1, QIN Feng1, ZHAO Long-lian2, LI Jun-hui2, MA Zhan-hong1, WANG Hai-guang1* . Identification and Classification of Disease Severity of Wheat Stripe Rust Using Near Infrared Spectroscopy Technology . SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(02): 367-371.
[1] Chen X M. Can. J. Plant Pathol., 2005, 27(3): 314. [2] Line R F. Annu. Rev. Phytopathol., 2002, 40: 75. [3] 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. [4] Wan A, Zhao Z, Chen X, et al. Plant Disease, 2004, 88(8): 896. [5] Wan A M, Chen X M, He Z H. Australian Journal of Agricultural Research, 2007, 58(6): 605. [6] 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. [7] 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. [8] 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. [9] Chen Q S, Zhao J W, Liu M H, et al. Journal of Pharmaceutical and Biomedical Analysis, 2008, 46(3): 568. [10] Alamprese C, Casale M, Sinelli N, et al. LWT-Food Science and Technology, 2013, 53(1): 225. [11] Sims D A, Gamon J A. Remote Sensing of Environment, 2002, 81(2-3): 337. [12] CHEN Bing, LI Shao-kun, WANG Ke-ru, et al(陈 兵，李少昆，王克如，等). Scientia Agricultura Sinica(中国农业科学), 2007, 40(12): 2709. [13] CHEN Bing, WANG Ke-ru, LI Shao-kun, et al(陈 兵，王克如，李少昆，等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2011, 27(9): 86. [14] FENG Lei, CHEN Shuang-shuang, FENG Bin, et al(冯 雷，陈双双，冯 斌，等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报)，2012, 43(8): 175. [15] Chang C C, Lin C J. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): 27.
