Deoxynivalenol (DON) accumulation in wheat due to Fusarium Head Blight (FHB) negatively affects grain quality and subsequently reduces grain yield. Evaluation of DON is an integral part in breeding FHB-resistant wheat varieties. Here we explored the potential of hyperspectral imaging to indirectly detect and predict DON concentration. A total of 172 wheat genotypes evaluated for DON concentration using GC/MS (Liquid Chromatography – Mass Spectrometry). Hyperspectral imaging of Fusarium-damaged wheat kernels (FDKs) for each genotype was carried out using a handheld hyperspectral imaging camera, Specim IQ (Specim Ltd., Oulo, Finland). Processing of hyperspectral images was carried out using QGIS 3.10.2 and spectral reflectance values were carried out using Raster package in R. Of the 204 wavebands (397 nm – 1004 nm) generated, genotypes showed significant variation (p-value < 0.05) in 196 wavebands. However, only the first 130 wavebands (397 nm – 778 nm) were used for further analysis due to obvious noise in the remaining wavebands. Pearson’s Correlation revealed significant correlation (p-value < 0.05) between DON concentration and reflectance values in all the 130 wavebands (r=0.32 to r=62). All 130 wavebands were used in a simple Linear Regression Model generating an r² value of 0.95. A cross validation accuracy of actual vs. predicted value yielded an r and r² value of 0.73 and 0.53, respectively. Similarly, Ridge Regression Best Linear Unbiased Prediction (rrBLUP) yielded a cross validation accuracy of r=0.75. Five wavebands: 622, 619, 628, 613, and 616 were identified through feature selection to have the most contribution to observed variation. Taking the results into account, this study has demonstrated the potential use of hyperspectral imaging in detecting and predicting DON Concentration in Fusarium-damaged wheat kernels.