|
|
|
|
|
|
Recognition Method of Cucumber Leaves Diseases Based on Visual Spectrum and Support Vector Machine |
LI Xin-xing1, ZHU Chen-guang1, BAI Xue-bing1, MAO Fu-huan1, FU Ze-tian1,2, ZHANG Ling-xian1* |
1. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
2. Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing 100083, China |
|
|
Abstract In this paper, we used cucumber leaves disease as the research object, and identified cucumber leaves disease based on the difference of visible spectral reflectance. The support vector machine recognition is an efficient recognition method, which is always used as identification model. For cucumber leaves diseases, if we constructed the support vector machine based on digital image process, we can get accurate and efficient recognition. Consequently, this paper studied the cucumber leaves disease recognition method based on support vector machine. Firstly, the method of wavelet domain denoising was applied to image denoising. The segmentation results were compared with K mean clustering, OTSU and edge segmentation. The results showed that K-means clustering method was more accurate. We extracted texture, color and shape feature parameters, 15 feature parameters. Then, the optimal parameters of c and g were selected by cross-validation, and the parameters of the kernel function were optimized and using RBF kernel to construct SVM classifier. By comparing the linearity kernel, polynomial kernel and RBF kernel of the SVM recognition’s correct rate, we got that the RBF kernel is most accurate for the recognition of the cucumber leaf disease. Therefore, we used RBF kernel to construct SVM classifier. Finally, there was an identification model of cucumber leaf disease which was based on SVM classifier, and two other efficient identification models, back Propagation neural network, fuzzy clustering identification model. We constructed three kinds of identification models through comparing the correct recognition rate and running time. The results of the test showed that the cucumber downy mildew's correct recognition rate based on SVM classifier was 95%. The correct recognition rate of cucumber powdery mildew and brown spot was 90%, and the average diagnosis accuracy was 92%. In addition, the method running time was the shortest. In summary, the results show that, among the three recognition methods, cucumber leaves disease recognition based on the SVM classifier is the most suitable, demonstrating that the method can be used to rapidly identify cucumber leaves diagnosis based on visual spectrum.
|
Received: 2017-12-02
Accepted: 2018-05-15
|
|
Corresponding Authors:
ZHANG Ling-xian
E-mail: zlx131@163.com
|
|
[1] MA Jun-cheng, WEN Han-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.
[2] YANG Xing-chuan, LUO Hong-xia, ZHAO Wen-ji, et al(杨兴川,罗红霞,赵文吉,等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(9): 2873.
[3] Xie C, He Y. Sensors, 2016, 16(5): 676.
[4] Yuan L, Huang Y, Loraamm R W, et al. Field Crops Research, 2014, 156: 199.
[5] Zhang X, Liu F, He Y, et al. Biosystems Engineering, 2013, 115(1): 56.
[6] ZAI Song-mei, WEN Ji, GUO Dong-dong, et al(宰松梅,温 季,郭冬冬,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2011, 27 (3): 237.
[7] Yang C, Odvody G N, Fernandez C J, et al. Precision Agriculture,2014, 16(2): 201.
[8] 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.
[9] Mohanty S P, Hughes D P, Salathé M. Frontiers in Plant Science, 2016, 7: 1419.
[10] 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(supl): 115.
[11] DING Yong-jun, ZHANG Jing-jing, LEE WonSuk, et al(丁永军, 张晶晶, LEE WonSuk, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2017,(9): 32.
[12] Bashish D, Braik M, Bani-Ahmad S. Information Technology Journal, 2011, 10(2): 267.
[13] Muthukannan K, Latha P, Selvi R P, et al. Journal of Engineering and Applied Sciences, 2015, 10(4): 1913.
[14] Verma T, Dubey S. Advances in Computational Sciences and Technology, 2017, 10(5): 721.
[15] Singh V, Misra A K. Information Processing in Agriculture, 2017, 4(1): 41.
[16] DENG Li-miao, TANG Jun, MA Wen-jie(邓立苗, 唐 俊, 马文杰). Journal of Chinese Agricultural Mechanization(中国农机化学报), 2014, 35(6): 72. |
[1] |
LI Yu1, ZHANG Ke-can1, PENG Li-juan2*, ZHU Zheng-liang1, HE Liang1*. Simultaneous Detection of Glucose and Xylose in Tobacco by Using Partial Least Squares Assisted UV-Vis Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 103-110. |
[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] |
LI Hu1, ZHONG Yun1, 2, FENG Ya-ting1, LIN Zhen1, ZHU Shi-jiang1, 2*. Multi-Vegetation Index Soil Moisture Inversion Model Based on UAV
Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 207-214. |
[4] |
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. |
[5] |
CHENG Hui-zhu1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, MA Qian1, 2, ZHAO Yan-chun1, 2. Genetic Algorithm Optimized BP Neural Network for Quantitative
Analysis of Soil Heavy Metals in XRF[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3742-3746. |
[6] |
SHEN Si-cong, ZHANG Jing-xue, CHEN Ming-hui, LI Zhi-wei, SUN Sheng-nan, YAN Xue-bing*. Estimation of Above-Ground Biomass and Chlorophyll Content of
Different Alfalfa Varieties Based on UAV Multi-Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3847-3852. |
[7] |
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. |
[8] |
LI Wen-wen1, 2, LONG Chang-jiang1, 2, 4*, LI Shan-jun1, 2, 3, 4, CHEN Hong1, 2, 4. Detection of Mixed Pesticide Residues of Prochloraz and Imazalil in
Citrus Epidermis by Surface Enhanced Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3052-3058. |
[9] |
LIU Fei1, TAN Jia-jin1*, XIE Gu-ai2, SU Jun3, YE Jian-ren1. Early Diagnosis of Pine Wilt Disease Based on Hyperspectral Data and Needle Resistivity[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3280-3285. |
[10] |
MA Qian1, 2, YANG Wan-qi1, 2, LI Fu-sheng1, 2*, CHENG Hui-zhu1, 2, ZHAO Yan-chun1, 2. Research on Classification of Heavy Metal Pb in Honeysuckle Based on XRF and Transfer Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2729-2733. |
[11] |
LÜ Shi-lei1, 2, 3, WANG Hong-wei1, LI Zhen1, 2, 3*, ZHOU Xu1, ZHAO Jing1. Hyperspectral Identification Model of Cantonese Tangerine Peel Based on BWO-SVM Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2894-2901. |
[12] |
WANG Jun-jie1, YUAN Xi-ping2, 3, GAN Shu1, 2*, HU Lin1, ZHAO Hai-long1. Hyperspectral Identification Method of Typical Sedimentary Rocks in Lufeng Dinosaur Valley[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2855-2861. |
[13] |
WANG Yi-ru1, GAO Yang2, 3, WU Yong-gang4*, WANG Bo5*. Study of the Electronic Structure, Spectrum, and Excitation Properties of Sudan Red Ⅲ Molecule Based on the Density Functional Theory[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2426-2436. |
[14] |
ZHU Yan-ping1, CUI Chuan-jin1*, CHENG Peng-fei1, 2, PAN Jin-yan1, SU Hao1, 2, ZHANG Yi1. Measurement of Oil Pollutants by Three-Dimensional Fluorescence
Spectroscopy Combined With BP Neural Network and SWATLD[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2467-2475. |
[15] |
LI Shu-fei1, LI Kai-yu1, QIAO Yan2, ZHANG Ling-xian1*. Cucumber Disease Detection Method Based on Visible Light Spectrum and Improved YOLOv5 in Natural Scenes[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2596-2600. |
|
|
|
|