 The study assesses the utility of multispectral UAV and VHRS imagery as a high throughput phenotyping HTP tool for Wittrass disease detection, using six breadwit varieties with differing rust resistance in a randomized trial. The study found that several spectral features demonstrated strong predictive power for the detection of combined Wittrass diseases and the estimation of variety's response to disease stress and grain yield, with visible spectral bands being more useful at booting and via senile vegetation indices at heading. The top performing spectral features for disease progression and grain yield were the red band and UAV derived RVI and NDVI, providing valuable insight into the upscaling capability of multispectral sensors for disease detection at early growth stages. This article was authored by Gerald Blash, Tedes Ambaba, Tamirat Nigash, and others.