Wheat Yellow Rust Detection Using UAV-Based Hyperspectral Technology

被引:136
作者
Guo, Anting [1 ,2 ]
Huang, Wenjiang [1 ,2 ,3 ]
Dong, Yingying [1 ,2 ]
Ye, Huichun [1 ,3 ]
Ma, Huiqin [1 ]
Liu, Bo [4 ]
Wu, Wenbin [5 ]
Ren, Yu [1 ,2 ]
Ruan, Chao [1 ,2 ]
Geng, Yun [1 ,2 ]
机构
[1] Chinese Acad Sci, Aerosp Informat Res Inst, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China
[2] Univ Chinese Acad Sci, Beijing 100190, Peoples R China
[3] Key Lab Earth Observat, Sanya 572029, Peoples R China
[4] Chinese Acad Agr Sci, Inst Plant Protect, State Key Lab Biol Plant Dis & Insect Pests, 2 West Yuanmingyuan Rd, Beijing 100193, Peoples R China
[5] Chinese Acad Agr Sci, Beijing 100081, Peoples R China
基金
中国国家自然科学基金; 国家重点研发计划;
关键词
UAV hyperspectral; wheat yellow rust; disease monitoring; vegetation index; texture; spatial resolution; LAUREL WILT DISEASE; REFLECTANCE MEASUREMENTS; CHLOROPHYLL CONTENT; SPATIAL-RESOLUTION; BIOMASS ESTIMATION; SPECTRAL INDEXES; POWDERY MILDEW; TEXTURE DATA; CANOPY; LEAF;
D O I
10.3390/rs13010123
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Yellow rust is a worldwide disease that poses a serious threat to the safety of wheat production. Numerous studies on near-surface hyperspectral remote sensing at the leaf scale have achieved good results for disease monitoring. The next step is to monitor the disease at the field scale, which is of great significance for disease control. In our study, an unmanned aerial vehicle (UAV) equipped with a hyperspectral sensor was used to obtain hyperspectral images at the field scale. Vegetation indices (VIs) and texture features (TFs) extracted from the UAV-based hyperspectral images and their combination were used to establish partial least-squares regression (PLSR)-based disease monitoring models in different infection periods. In addition, we resampled the original images with 1.2 cm spatial resolution to images with different spatial resolutions (3 cm, 5 cm, 7 cm, 10 cm, 15 cm, and 20 cm) to evaluate the effect of spatial resolution on disease monitoring accuracy. The findings showed that the VI-based model had the highest monitoring accuracy (R-2 = 0.75) in the mid-infection period. The TF-based model could be used to monitor yellow rust at the field scale and obtained the highest R-2 in the mid- and late-infection periods (0.65 and 0.82, respectively). The VI-TF-based models had the highest accuracy in each infection period and outperformed the VI-based or TF-based models. The spatial resolution had a negligible influence on the VI-based monitoring accuracy, but significantly influenced the TF-based monitoring accuracy. Furthermore, the optimal spatial resolution for monitoring yellow rust using the VI-TF-based model in each infection period was 10 cm. The findings provide a reference for accurate disease monitoring using UAV hyperspectral images.
引用
收藏
页码:1 / 22
页数:22
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