光谱学与光谱分析 |
|
|
|
|
|
Prediction of Greenhouse Cucumber Disease Based on Chlorophyll Fluorescence Spectrum Index |
SUI Yuan-yuan1, 2, WANG Qing-yu2*, YU Hai-ye1* |
1. College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China 2. College of Plant Science,Jilin University,Changchun 130022, China |
|
|
Abstract The occurrence of greenhouse vegetable diseases and its epidemic seriously affect the production and management of facility agriculture, which greatly reduce the economic benefits of facility agriculture. In order to achieve nondestructive and accurate prediction of greenhouse vegetable diseases, this paper taking cucumber downy mildew disease as the research object, constructed spectrum characteristic index by using chlorophyll fluorescence induced by laser and established the prediction model of greenhouse vegetable diseases. In this paper, the experiment used comparative analysis method. The healthy leaves of the crops were inoculated with the pathogen spores, the spectrum curves of four groups of test samples: healthy, 2 d inoculated, 6 d inoculated and the ones with obvious symptoms were collected; then qualitative analysis was given to the variation regulation of the fluorescence intensity with the leaf samples infected with the pathogen spores. The chlorophyll fluorescence spectrum index k1=F685/F512 and k2=F734/F512 were created by using the peak and valley values of different bands. According to the range of values, set k1=20 and k2=10 as the characteristic value to judge the sample with obvious symptoms or with no obvious symptoms, and the accuracy rate of the judgment was 96% and 94% respectively. Based on spectrum index created and the classification results of sample health status, we selected the spectrum index F685/F512, F685-F734, F715/F612 to determine the health status of the sample and selected spectrum index F685/F512, F734/F512, F685-F734, F715/F612 as the inputs of quantitative analysis model. Regarding classification accuracy of prediction set as the evaluation criteria, we compared three data modeling methods: discriminant analysis, BP neural network and support vector machine. The results showed that the forecasting ability can reach 91.38% when the support vector machine was used as the modeling method for predicting the downy mildew disease. Use the method with chlorophyll fluorescence induced by laser to construct spectrum index to study the prediction of plant diseases, which has a good classification and identification effect.
|
Received: 2015-03-19
Accepted: 2015-06-28
|
|
Corresponding Authors:
WANG Qing-yu, YU Hai-ye
E-mail: wqy414cn@yahoo.com.cn; haiye@jlu.edu.cn
|
|
[1] Lucas J A. Journal of Agricultural Science, 2011, 149: 91. [2] Chowdhury A, Pradhan S, Saha M, et al. Indian Journal of Microbiology, 2008, 48(1): 114. [3] Zhang Shouren. Chinese Bulletin of Botany, 1999, 16(4): 444. [4] Lichtenthaler H, Lang M, Sowinska M, et al. Journal of Plant Physiology, 1996, 148: 599. [5] Manda K, Saravanan R, Maitl S, et al. Journal of Plant Diseases and Protection, 2009, 116(4): 164. [6] Danilo C, Susan S, Mauro J, et al. Environmental and Experimental Botany, 2006,(60): 504. [7] Strauss A, Kruger G, Strasser R, et al. Environmental and Experimental Botany, 2006, 56: 147. [8] Benjamin A, Moses J, Elvis A, et al. Environmental Technology, 2012, 33(3): 367. [9] Adnan M, Erin E, Michael D, et al. Journal of Biological Engineering, 2013, 7: 5. [10] Astrid S, Iryna I T, Sebastian K, et al. Nematology, 2006, 8(2): 273. [11] 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. [12] Cristianini. N, Shawe-Taylor. J. An Introduction to Support Vector Machines and Other Kerner-Based Learning Methods(支持向量机导论). Beijing: Publishing House of Electronics Industry(北京: 电子工业出版社), 2004. |
[1] |
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. |
[2] |
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. |
[3] |
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. |
[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] |
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. |
[6] |
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. |
[7] |
GUO Zhou-qian1, 2, LÜ Shu-qiang1, 2, HOU Miao-le1, 2*, SUN Yu-tong1, 2, LI Shu-yang1, 2, CUI Wen-yi1. Inversion of Salt Content in Simulated Mural Based on Hyperspectral
Mural Salt Index[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3272-3279. |
[8] |
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. |
[9] |
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. |
[10] |
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. |
[11] |
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. |
[12] |
GAO Yu1, SUN Xue-jian1*, LI Guang-hua2, ZHANG Li-fu1, QU Liang2, ZHANG Dong-hui1, CHANG Jing-jing2, DAI Xiao-ai3. Study on the Derivation of Paper Viscosity Spectral Index Based on Spectral Information Expansion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2960-2966. |
[13] |
SONG Cheng-yang1, GENG Hong-wei1, FEI Shuai-peng2, LI Lei2, GAN Tian2, ZENG Chao-wu3, XIAO Yong-gui2*, TAO Zhi-qiang2*. Study on Yield Estimation of Wheat Varieties Based on Multi-Source Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2210-2219. |
[14] |
ZHANG Hai-liang1, XIE Chao-yong1, TIAN Peng1, ZHAN Bai-shao1, CHEN Zai-liang1, LUO Wei1*, LIU Xue-mei2*. Measurement of Soil Organic Matter and Total Nitrogen Based on Visible/Near Infrared Spectroscopy and Data-Driven Machine Learning Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2226-2231. |
[15] |
LI Hao-dong1, 2, LI Ju-zi1*, CHEN Yan-lin1, HUANG Yu-jing1, Andy Hsitien Shen1*. Establishing Support Vector Machine SVM Recognition Model to Identify Jadeite Origin[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2252-2257. |
|
|
|
|