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Using Hyperspectral Remote Sensing Identification of Wheat Take-All Based on SVM

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Abstract

Wheat take-all is quarantine diseaseand took place more and more severer in recent years, It is important to monitor it effectively. This article using hyperspectral remote sensing, through the different levels of the incidence of wheat take-all canopy spectral reflectance data collection analysis and processing, using support vector machine(SVM) classification method to build Wheat Take-all disease level prediction model for the prediction and prevention for wheat take-all to provide technical support. Results shows that the wheat canopy spectral reflectance change significantly under the influence of the disease; through data analysis, choose 700~900nm wavelength band training as sensitive to model the performance of the best results; Upon examination, constructed the forecasting model based on this band to predict when the predicted value and the actual value of the correlation coefficient up to 0.9434. The results of this study will not only provide theoretical and technical support for wheat no-destructive detection and safety production, but also shed light on the development of novel strategy to detect and control crop pest and disease, which has great significance to the food safety.

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Authors and Affiliations

  1. College of Information and Management Science, Henan Agriculture University, Zhengzhou, 450046, China

    Hongbo Qiao, Hongtao Jiao, Yue Shi, Lei Shi, Wei Guo & Xinming Ma

Authors
  1. Hongbo Qiao

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  2. Hongtao Jiao

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  3. Yue Shi

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  4. Lei Shi

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  5. Wei Guo

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  6. Xinming Ma

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Corresponding author

Correspondence toHongbo Qiao.

Editor information

Editors and Affiliations

  1. China Agricultural University, Beijing, China

    Daoliang Li

  2. China Agricultural University, Beijing, China

    Yingyi Chen

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© 2015 IFIP International Federation for Information Processing

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Qiao, H., Jiao, H., Shi, Y., Shi, L., Guo, W., Ma, X. (2015). Using Hyperspectral Remote Sensing Identification of Wheat Take-All Based on SVM. In: Li, D., Chen, Y. (eds) Computer and Computing Technologies in Agriculture VIII. CCTA 2014. IFIP Advances in Information and Communication Technology, vol 452. Springer, Cham. https://doi.org/10.1007/978-3-319-19620-6_3

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