Real-Time Monitoring Technology of Potato Pests and Diseases in Northern Shaanxi Based on Hyperspectral Data

  • Yong-heng ZhangEmail author
  • Xiao-yan Ai
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 279)


When using traditional monitoring technology to monitor the disaster area of potato in Northern Shaanxi, there was a problem of insufficient monitoring accuracy. In view of the above problems, a real-time monitoring technology for potato pests and diseases based on hyperspectral data is put forward. Firstly, the geological environment of the monitoring area is briefly introduced. Hyper Spectral Remote Sensing is used to obtain the hyperspectral data of the damaged area of the potato in the study area, and pretreatment is performed to establish a regression model. Finally, the pre-processed hyperspectral data is obtained. Substituting data into the model, the area of potato pests and diseases in the research area is obtained. The results showed that the accuracy of the method was 20.29% higher than that of the traditional potato pest and disease monitoring technology, and the accurate monitoring of the disaster area was realized. It has practicality and superiority.


Hyperspectral data Potato Diseases and insect pests Affected area Monitor 


Fund Project

Agricultural Science Research Plan in Shaanxi Province of China: “Research on key technologies and application of Intelligent Prediction and Forecasting of Potato diseases and pests based on the Internet of Things” (NO. 2016NY141).


  1. 1.
    Guo, H.Y., Liu, G.H., Wu, L.G., et al.: Hyper-spectral imaging technology for nondestructive detection of potato ring rot. Food Sci. 37(12), 203–207 (2016)Google Scholar
  2. 2.
    Bai, X.Q., Zhang, X.L., Zhang, N., et al.: Monitoring model of Dendrolimus tabulaeformis disaster using hyperspectral remote sensing technology. J. Beijing For. Univ. 38(11), 16–22 (2016)Google Scholar
  3. 3.
    Xu, M.Z., Li, M., Bai, Z.P., et al.: Identification of early blight disease on potato leaves using hyperspectral imaging technique. J. Agric. Mechanization Res. 38(6), 205–209 (2016)Google Scholar
  4. 4.
    Zhao, M.F., Liu, Z.D., Zou, X., et al.: Detection of defects on potatoes by hyperspectral imaging technology. Laser J. 37(3), 20–24 (2016)Google Scholar
  5. 5.
    He, C., Zheng, S.L., Zhou, S.M., et al.: Estimation models of chlorophyll contents in potato leaves based on hyperspectral vegetation indices. J. South China Agric. Univ. 37(5), 45–49 (2016)Google Scholar
  6. 6.
    Hu, Y.H., Ping, X.W., Xu, M.Z., et al.: Detection of late blight disease on potato leaves using hyperspectral imaging technique. Spectrosc. Spectr. Anal. 36(2), 515–519 (2016)Google Scholar
  7. 7.
    Li, X.Y., Xu, S.M., Feng, Y.Z., et al.: Detection of potato slight bruise based on hyperspectral image and fruit fly optimization algorithm. Trans. Chinese Soc. Agric. Mach. 47(1), 221–226 (2016)Google Scholar
  8. 8.
    Shi, F.F., Gao, X.H., Yang, L.Y., et al.: Identifying typical crop types from ground hyper-spectral data: a case study in the Huangshui river basin, Qinghai province. Geogr. Geo Inf. Sci. 32(2), 32–39 (2016)Google Scholar
  9. 9.
    Package seventy-three.: Spectral Image Analysis of Chinese Medicine Component Content Detection. Comput. Simul. 34(07), 369–372 (2017)Google Scholar
  10. 10.
    Wang, G.B., Liu, W., Ming-Shan, L.I.: Green control technology of rice pests and diseases and integrated demonstration. J. Anhui Agric. Sci. 46(09), 269–271 (2017)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.Yulin UniversityYulinChina

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