Improved early detection of wheat stripe rust through integration pigments and pigment-related spectral indices quantified from UAV hyperspectral imagery

Abstract

Wheat stripe rust is a significant disease affecting wheat growth, often referred to as the “cancer of wheat”. Early and accurate detection of stripe rust is crucial for enabling crop managers to implement effective control measures. Hyperspectral remote sensing methods for crop disease detection have gained significant attention. However, commonly used spectral bands or spectral indices (SIs) from hyperspectral data often fail to capture the subtle changes associated with the early stages of crop diseases accurately. In this study, we propose a method for early detection of wheat stripe rust by combining pigments and SIs retrieved from UAV hyperspectral imagery. We acquired hyperspectral images of wheat stripe rust at 7, 16, and 23 days post-inoculation (DPI) using a UHD 185 hyperspectral sensor (450–950 nm) mounted on an S1000 hexacopter UAV. Pigments, including chlorophylls (Cab), carotenoids (Car), anthocyanins, Cab/Car, and 11 pigment-related SIs, were extracted from UAV hyperspectral images using radiative transfer modeling. The early detection model for wheat stripe rust was developed using these parameters and machine learning algorithms. The results indicated selected pigments and SIs effectively distinguished stripe rust-infected wheat from healthy wheat at 7, 16, and 23 DPI. Models that combine pigments and SIs (PSIMs) perform better than those relying solely on SIs (SIMs) or pigments (PMs). Notably, the RF-based PSIM achieved overall accuracies of 78.1 % and 81.3 % during the asymptomatic (7 DPI) and minimally symptomatic (16 DPI) phases of disease, respectively. Additionally, the pigments in the PSIM contributed more significantly than the SIs, highlighting the importance of pigments in the early detection of stripe rust. Overall, the method combining pigments and spectral indices proposed in this study effectively enhances the early detection of wheat stripe rust and offers valuable insights into the early detection of other crop diseases.

Keywords

• Wheat stripe rust
• Early detection
• UAV hyperspectral images
• Pigments
• Spectral indices
• Radiative transfer model