A unified heuristic approach to simultaneously detect fusarium and ergot damage in wheat

Fusarium spp. and Ergot (Claviceps purpurea) are fungal pathogenic species that infect cereals worldwide, causing widespread economic losses. Fusarium-damaged kernels (FDKs) in wheat often contain harmful mycotoxins, while Ergot damage (ERG) is commonly characterized by the presence of alkaloids. Both FDK and ERG damage could cause health hazards to people and animals. Currently, assessment of FDKs and ERG is performed by human visual inspection using distinctly different biochemical assays and/or wet chemistry methods, all of which are subjective, time-consuming and non-repeatable. In this study, the authors present a unified non-destructive optical method using visible-near-infrared (VNIR) (400-1000 nm) and short-wavelength near-infrared (SWIR) (1000-2500 nm) hyperspectral imaging (HI) to simultaneously detect FDK and ERG damage in wheat kernels. The un-mixing technique of spectral angle mapping (SAM) was employed for pixel- and object-wise classification of the three classes i.e. FDK, ERG and control (healthy), of non-touching wheat kernels. Overall classification accuracies of VNIR–93% and SWIR–85% were obtained with 100% classification of ERG samples in the VNIR range. To reduce model complexity, principle component analysis (PCA) was explored, and a subset of 12 significant wavelengths was selected. The SAM model based on these selected wavelengths did not achieve accurate prediction, indicating it requires the entire spectral data for reliable performance. However, a partial least square discriminant analysis (PLSDA) model using the reduced wavelength subset achieved overall classification accuracies of VNIR–94% and SWIR–90%, which were better when compared to the SAM method. This study is the first known attempt to simultaneously identify two important fungal diseases in wheat using a completely non-invasive and non-destructive method. The results confirm the feasibility of developing a fast and cost-effective multi-spectral classification technique for FDK and ERG detection.