Ramularia collo-cygni is the biotic factor responsible for the disease Ramularia leaf spot (RLS) of barley (Hordeum vulgare). Despite having been described over 100 years ago and being considered a minor disease in some countries, the fungus is attracting interest in the scientific community as a result of the increasing number of recorded economically damaging disease epidemics. New reports of disease spread and fungal identification using molecular diagnostics have helped redefine RLS as a global disease. This review describes recent developments in our understanding of the biology and epidemiology of the fungus, outlines advances made in the field of the genetics of both the fungus and host, and summarizes the control strategies currently available.

In this paper, thermal (8-13 µm) and hyperspectral imaging in visible and near infrared (VNIR) and short wavelength infrared (SWIR) ranges were used to elaborate a method of early detection of biotic stresses caused by fungal species belonging to the genus Alternaria that were host (Alternaria alternata, Alternaria brassicae, and Alternaria brassicicola) and non-host (Alternaria dauci) pathogens to oilseed rape (Brassica napus L.). The measurements of disease severity for chosen dates after inoculation were compared to temperature distributions on infected leaves and to averaged reflectance characteristics. Statistical analysis revealed that leaf temperature distributions on particular days after inoculation and respective spectral characteristics, especially in the SWIR range (1000-2500 nm), significantly differed for the leaves inoculated with A. dauci from the other species of Alternaria as well as from leaves of non-treated plants. The significant differences in leaf temperature of the studied Alternaria species were observed in various stages of infection development. The classification experiments were performed on the hyperspectral data of the leaf surfaces to distinguish days after inoculation and Alternaria species. The second-derivative transformation of the spectral data together with back-propagation neural networks (BNNs) appeared to be the best combination for classification of days after inoculation (prediction accuracy 90.5%) and Alternaria species (prediction accuracy 80.5%).