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

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Advanced Hybrid Information Processing (ADHIP 2018)

Abstract

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.

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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).

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Correspondence to Yong-heng Zhang .

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

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Zhang, Yh., Ai, Xy. (2019). Real-Time Monitoring Technology of Potato Pests and Diseases in Northern Shaanxi Based on Hyperspectral Data. In: Liu, S., Yang, G. (eds) Advanced Hybrid Information Processing. ADHIP 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 279. Springer, Cham. https://doi.org/10.1007/978-3-030-19086-6_12

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  • DOI: https://doi.org/10.1007/978-3-030-19086-6_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-19085-9

  • Online ISBN: 978-3-030-19086-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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