Imaging techniques can be used to evaluate the quality and safety of agricultural products. Fusarium head blight (FHB) results in reduced barley yields and also diminished value of harvested barley. Deoxynivalenol (DON) is a mycotoxin produced by the causal Fusarium species that pose health risks to humans and livestock. DON has currently measured via gas chromatography (GC) methods that are time-consuming and expensive. We seek to apply imaging technology to rapidly and non-destructively quantify DON in high throughput and less expensive method. The feasibility of hyperspectral imaging to determine DON contents of barley kernels was evaluated using machine learning algorithms. Partial least square discriminant analysis (PLSDA) was able to discriminate kernels into four separate classes corresponding to their DON levels. Barley kernels could be classified as having low (<5 ppm) or high DON levels, with Matthews's correlation coefficient in cross-validation (M-RCV) of as high as 0.823. PLSR showed good performance in linear algorithms for DON detection, but higher accuracy was obtained by non-linear algorithms, including weighted partial least squares regression (LWPLSR), support vector machine regression (SVMR), and artificial neural network (ANN). Among all algorithms, the non-linear LWPLSR achieved the highest accuracy, with the coefficient of determination in prediction (R2 P) of 0.728 and root mean square error of prediction (RMSEP) of 3.802. The results demonstrate that hyperspectral imaging and machine learning algorithms have the potential to assist the FHB resistance breeding process by accelerating the quantification of DON in barley samples.