Wheat stripe rust remote sensing monitoring based on a new vegetation index

Jia He; Laigang Wang; Yan Guo; Yan Zhang; Hongli Zhang; Shaobo Xu

doi:10.1117/12.2657093

18 October 2022 Wheat stripe rust remote sensing monitoring based on a new vegetation index

Jia He, Laigang Wang, Yan Guo, Yan Zhang, Hongli Zhang, Shaobo Xu

Proceedings Volume 12349, International Conference on Agri-Photonics and Smart Agricultural Sensing Technologies (ICASAST 2022); 1234905 (2022) https://doi.org/10.1117/12.2657093
Event: International Conference on Agri-Photonics and Smart Agricultural Sensing Technologies (ICASAST 2022), 2022, Zhengzhou, China

Abstract

Stripe rust is a common disease of wheat, and rapid and accurate identification of the damage range of stripe rust at regional scale is of great significance for disease control and prevention. In this study, a new Wheat stripe rust optimized vegetation index (WSROVI) was constructed based on Sentinel-2 remote sensing images and Wheat crown hyperspectral data. By setting a reasonable WSROVI index for threshold segmentation, the incidence area of wheat stripe rust was extracted and the accuracy was verified by ground survey data. The results showed that the user accuracy and mapping accuracy of the spots of stripe rust extracted based on WSROVI were 85.11% and 81.63%, respectively, and the user accuracy and mapping accuracy of the healthy spots were 75.68% and 80.00%, respectively, indicating that the index could be used as a monitoring index of regional stripe rust. This method is simple and efficient, which can provide effective theoretical basis and technical support for regional scale remote sensing monitoring of wheat stripe rust.

Citation Download Citation

Jia He, Laigang Wang, Yan Guo, Yan Zhang, Hongli Zhang, and Shaobo Xu "Wheat stripe rust remote sensing monitoring based on a new vegetation index", Proc. SPIE 12349, International Conference on Agri-Photonics and Smart Agricultural Sensing Technologies (ICASAST 2022), 1234905 (18 October 2022); https://doi.org/10.1117/12.2657093

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KEYWORDS

Remote sensing

Reflectivity

Vegetation

Agriculture

Satellites

Satellite imaging

Multispectral imaging

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