Application of NDVI for Early Detection of Yellow Rust (Puccinia striiformis)

Yellow rust is one of the most destructive fungal diseases affecting wheat, significantly reducing yield and grain quality. Early detection is crucial for effective plant protection and disease management. This study aims to develop and validate a methodology for early diagnosis of yellow rust using the Normalized Difference Vegetation Index (NDVI) derived from UAV-acquired spectral data. This research was conducted in an experimental wheat field near General Toshevo, Bulgaria, which is owned by the Dobrudja Agricultural Institute (DAI). A widely cultivated winter wheat variety, Enola, was monitored using UAV-based imaging, and the NDVI values were analyzed to assess the correlation between spectral reflectance and infection severity. The NDVI showed a moderate correlation as an indicator of pathogen-induced stress, with moderate predictive capability (R2 = 51.4%) for assessing yellow rust infection severity. The results demonstrated that UAV-based NDVI analysis could effectively detect early-stage infections and monitor the spatial spread of the disease. The proposed methodology enables large-scale, non-invasive monitoring of wheat health, facilitating early disease detection. This approach can help optimize disease management strategies, although ground-based validation remains essential to distinguish between different stress factors affecting vegetation.