Whitened kernel surface: A fast and reliable method for assessing Fusarium severity on cereal grains by digital picture analysis

Fusarium head blight (FHB) is a cereal disease of major importance responsible for yield losses and mycotoxin contaminations in grains. Here, we introduce a new measurement approach to quantify FHB severity on grains based on the evaluation of the whitened kernel surface (WKS) using digital image analysis. The applicability of WKS was assessed on two bread wheat and one triticale grain sample sets (265 samples). Pearson correlation coefficients between Fusarium‐damaged kernels (FDK) and WKS range from r = 0.77 to r = 0.81 and from r = 0.61 to r = 0.86 for the correlation between deoxynivalenol (DON) content and WKS. This new scoring method facilitates fast and reliable assessment of the resistance to kernel infection and shows significant correlation with mycotoxin content. WKS can be automated and does not suffer from the “human factor” inherent to visual scorings. As a low‐cost and fast approach, this method appears particularly attractive for breeding and genetic analysis of FHB resistance where typically large numbers of experimental lines need to be evaluated, and for which WKS is suggested as an alternative to visual FDK scorings.     

Evaluation of fineness of wrinkles on husks of malting barley (Hordeum vulgare L.) by texture analysis of digital image data

Summary It has been suggested that the selection based on the wrinkles on the husks of malting barley is useful especially during the early generation in breeding program, because the wrinkles can be an indicator of malting quality of grains. However, it can be done only visually and requires much labor. In this study, the evaluation of the wrinkles by texture analysis of digital image data, was examined for the quantitative evaluation of the fineness of the wrinkles and its automatic selection. The texture analysis based on co-occurrence matrices was applied and it was revealed that finely wrinkled grains can be clearly discriminated by discriminant analysis on several parameters estimated from the co-occurrence matrices.