Downy mildew, caused by Hyaloperonospora parasitica, poses a significant threat to Brassica oleracea crops, leading to substantial reductions in yield and marketability. This study seeks to assess various vegetation indices for detecting different levels of downy mildew infection in a Brassica variety, Mildis, using hyperspectral data. Through artificial inoculation with H. parasitica sporangia suspension, distinct levels of downy mildew disease were induced. Spectral measurements, ranging from 350 nm to 1050 nm, were performed on the leaves under controlled environmental conditions, and reflectance data were collected and processed. The Successive Projections Algorithm (SPA) and signal sensitivity calculations were employed to identify the most informative wavelengths, which were then used to develop Downy Mildew Indices (DMI). A total of 37 existing vegetation indices and three proposed DMIs were evaluated to assess downy mildew infection levels. The results revealed that a support vector machine achieved accuracies of 71.3%, 80.7%, and 85.3% in distinguishing healthy leaves from those with early (DM1), progressed (DM2), and severe (DM3) infections, respectively, using the proposed downy mildew index. The development of this novel downy mildew index has the potential to facilitate the creation of an automated monitoring system for downy mildew and aid in resistance profiling in Brassica breeding lines.
