Research on Cucumber Powdery Mildew Recognition Based on Visual Spectra
WANG Xiang-yu1,2, ZHU Chen-guang1, FU Ze-tian1, ZHANG Ling-xian1, LI Xin-xing1*
1. Beijing Laboratory of Food Quality and Safety,College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
2. Department of Electronic Information and Physics, Changzhi University, Changzhi 046011, China
Abstract:Powdery mildew is one of the common diseases of cucumber, which has a rapid propagation speed and can cause a large reduction of cucumber. Quick and accurate recognition of cucumber powdery mildew has great significance for the diagnosis and control of cucumber diseases. Utilize visible spectrum technology combined with principal component analysis and support vector machine algorithm can realize the quick recognition of cucumber powdery mildew. Sphaerotheca fuliginea was used to make spore suspension and inoculated it into the cucumber leaves in a scientific research solar greenhouse to induce powdery mildew. When the powdery mildew occurred, the spectral information of cucumber leaves was collected by the Ocean Optics USB2000+ portable spectrometer. Five point sampling method was used to collect samples, two cucumber plants were inspected at each point and four leaves were checked on each plant, and five areas were chosen randomly on each leaf to use to spectral information acquisition. Then 200 samples of cucumber powdery mildew leaves were got , and 200 healthy leaf samples were collected as contrast by the same method. the standard white plate and dark current was Utilized to calibrate the spectrometer. The integral time and the scanning times were adjusted and the smoothness parameters of Ocean Optics Spectra-Suite software was used to smooth spectral curves and suppress noise. Through classification and recognition of spectral features, the spectral bands with big noise was removed and the 450~780 nm visible light band was chosen as the research range. The principal component analysis (PCA) was used to reduce the dimension of high-dimensional spectral data (947 dimension). According to the cumulative contribution rate of principal components, the former 5 principal components were chosen as input variables and the discriminant results as the output to build the classification model. We utilized support vector machine (SVM) algorithm and randomly took 120 samples as the training set to build the classification model, and the rest 80 samples as testing set for model checking, and the optimal model was obtained by selecting different kernel functions. The confusion matrix was used to evaluate the accuracy of the classification model, when the radial basis kernel function was selected, the recognition accuracy of the classification model for cucumber healthy leaves and powdery mildew leaves were respectively 100% and 96.25%, and the total accuracy was 98.125%. The results showed that the visible light spectrum analysis combined with PCA and SVM algorithm could be used to identify cucumber powdery mildew quickly and accurately, which provides a method and reference for the diagnosis of cucumber diseases.
Key words:Visible spectrum; Disease recognition; Principal component analysis; Support vector machine
王翔宇,朱晨光,傅泽田,张领先,李鑫星. 基于可见光光谱分析的黄瓜白粉病识别研究[J]. 光谱学与光谱分析, 2019, 39(06): 1864-1869.
WANG Xiang-yu, ZHU Chen-guang, FU Ze-tian, ZHANG Ling-xian, LI Xin-xing. Research on Cucumber Powdery Mildew Recognition Based on Visual Spectra. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2019, 39(06): 1864-1869.
[1] ZHANG Peng, ZHU Yu-qiang, WANG Li-li, et al(张 鹏,朱育强,王丽莉,等). Chinese Agricultural Science Bulletin(中国农学通报), 2017, 33(21): 134.
[2] GUAN Hui, ZHANG Chang-li, ZHANG Chun-yuan(关 辉,张长利,张春媛). Journal of Agricultural Mechanization Research(农机化研究), 2010, (3): 94.
[3] MA Jun-cheng, WEN Hao-jie, LI Xin-xing, et al(马浚诚,温皓杰,李鑫星,等). Transactions of The Chinese Society of Agricultural Machinery(农业机械学报), 2017, 48(2): 195.
[4] JIA Jian-nan, Ji Hai-yan(贾建楠,吉海彦). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2013, 29(S1): 115.
[5] Zhang S, Wu X, You Z, et al. Computers and Electronics in Agriculture, 2017, 134: 135.
[6] SUN Xu-dong, LIU Yan-de, XIAO Huai-chun, et al(孙旭东,刘燕德,肖怀春,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(2): 551.
[7] WANG You-ping, ZHU Jin-ying, GAO Ping-yin, et al(王友平,朱金英,高平银,等). Journal of Changjiang Vegetables, 2009, (1): 37.
[8] GAO Shi-gang, LUO Jin-yan, ZENG Rong, et al(高士刚,罗金燕,曾 蓉,等). Journal of Plant Protection, 2017, 44(5): 779.
[9] ZHANG Yin, ZHOU Meng-ran(张 银,周孟然). Infrared Technology(红外技术), 2007, 29(6): 345.
[10] LI Hong-lian, GONG Dong-jun, CAI Duan-bo, et al(李红莲,贡东军,蔡端波,等). Laser Journal, 2015, 36(10): 65.
[11] ZHANG Jian-hua, KONG Fan-tao, LI Zhe-min, et al(张建华,孔繁涛,李哲敏,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2014, 30(19): 222.
[12] WANG Jin-jing, ZHAO De-an, JI Wei(王津京,赵德安,姬 伟). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2009, 40(1): 148.
[13] Vapnik V. The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
[14] Cortes C, Vapnik V. Machine Learning 1995, 20(3): 273.
[15] Cherkassky V, Mulier F. Learning from Data: Concepts, Theory and Methods. New York: John Wiley & Sons, 1997.
[16] ZHANG Xue-gong(张学工). Acta Automatica Sinica(自动化学报), 2000, 26(1): 36.
[17] ZHU Yi-ning, YANG Ping, YANG Xin-yan, et al(朱毅宁,杨 平,杨新艳,等). Chinese Journal of Analytical Chemistry(分析化学), 2017, 45(3): 336.
[18] YIN Fei, FENG Da-zheng(尹 飞,冯大政). Computer Technology and Development(计算机技术与发展), 2008, 18(10): 31.