TY  - JA
AU  - Qiong Zheng
AU  - Wenjiang Huang
AU  - Huichun Ye
AU  - Yingying Dong
AU  - Yue Shi
AU  - Shuisen Chen
TI  - Using continous wavelet analysis for monitoring wheat yellow rust in different infestation stages based on unmanned aerial vehicle hyperspectral images
SN  - 1559-128X
PY  - 2020///
CY  - USA
PB  - OSA Publishing
KW  - AGROVOC
KW  - Monitoring
KW  - Plant diseases
KW  - Unmanned aerial vehicles
N1  - Peer review
N2  - Yellow rust is the most extensive disease in wheat cultivation, seriously affecting crop quality and yield. This study proposes sensitive wavelet features (WFs) for wheat yellow rust monitoring based on unmanned aerial vehicle hyperspectral imagery of different infestation stages [26 days after inoculation (26 DAI) and 42 DAI]. Furthermore, we evaluated the monitoring ability of WFs and vegetation indices on wheat yellow rust through linear discriminant analysis and support vector machine (SVM) classification frameworks in different infestation stages, respectively. The results show that WFs-SVM have promising potential for wheat yellow rust monitoring in both the 26 DAI and 42 DAI stages
DO  - https://doi.org/10.1364/AO.397844
T2  - Applied Optics
ER  -