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Remote Sensing
Volume 13
Issue 16
10.3390/rs13163185
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Article

Extraction of Areas of Rice False Smut Infection Using UAV Hyperspectral Data

by
Gangqiang An
1,2,
Minfeng Xing
1,2,*,
Binbin He
1,2,
Haiqi Kang
3,
Jiali Shang
4,
Chunhua Liao
5,
Xiaodong Huang
6 and
Hongguo Zhang
1
1
School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, China
2
The Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China
3
Crop Research Institute, Sichuan Academy of Agricultural Sciences, Chengdu 610066, China
4
Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, 960 Carling Avenue, Ottawa, ON K1A 0C6, Canada
5
School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai 519082, China
6
Applied Geosolutions, 15 Newmarket Road, Durham, NH 03824, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(16), 3185; https://doi.org/10.3390/rs13163185
Submission received: 13 May 2021 / Revised: 8 August 2021 / Accepted: 9 August 2021 / Published: 11 August 2021
(This article belongs to the Special Issue UAV Imagery for Precision Agriculture)
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Abstract

Rice false smut (RFS), caused by Ustilaginoidea virens, is a significant grain disease in rice that can lead to reduced yield and quality. In order to obtain spatiotemporal change information, multitemporal hyperspectral UAV data were used in this study to determine the sensitive wavebands for RFS identification, 665–685 and 705–880 nm. Then, two methods were used for the extraction of rice false smut-infected areas, one based on spectral similarity analysis and one based on spectral and temporal characteristics. The final overall accuracy of the two methods was 74.23 and 85.19%, respectively, showing that the second method had better prediction accuracy. In addition, the classification results of the two methods show that the areas of rice false smut infection had an expanding trend over time, which is consistent with the natural development law of rice false smut, and also shows the scientific nature of the two methods.
Keywords: UAV; hyperspectral data; rice; rice false smut UAV; hyperspectral data; rice; rice false smut

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MDPI and ACS Style

An, G.; Xing, M.; He, B.; Kang, H.; Shang, J.; Liao, C.; Huang, X.; Zhang, H. Extraction of Areas of Rice False Smut Infection Using UAV Hyperspectral Data. Remote Sens. 2021, 13, 3185. https://doi.org/10.3390/rs13163185

AMA Style

An G, Xing M, He B, Kang H, Shang J, Liao C, Huang X, Zhang H. Extraction of Areas of Rice False Smut Infection Using UAV Hyperspectral Data. Remote Sensing. 2021; 13(16):3185. https://doi.org/10.3390/rs13163185

Chicago/Turabian Style

An, Gangqiang, Minfeng Xing, Binbin He, Haiqi Kang, Jiali Shang, Chunhua Liao, Xiaodong Huang, and Hongguo Zhang. 2021. "Extraction of Areas of Rice False Smut Infection Using UAV Hyperspectral Data" Remote Sensing 13, no. 16: 3185. https://doi.org/10.3390/rs13163185

APA Style

An, G., Xing, M., He, B., Kang, H., Shang, J., Liao, C., Huang, X., & Zhang, H. (2021). Extraction of Areas of Rice False Smut Infection Using UAV Hyperspectral Data. Remote Sensing, 13(16), 3185. https://doi.org/10.3390/rs13163185

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