| 光谱学与光谱分析 |
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| 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, China2. College of Plant Science，Jilin University，Changchun 130022, China |
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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.
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Received: 2015-03-19
Accepted: 2015-06-28
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Corresponding Authors:
WANG Qing-yu, YU Hai-ye
E-mail: wqy414cn@yahoo.com.cn; haiye@jlu.edu.cn
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[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. |
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