Wheat infections caused by fungi of the genus Fusarium decrease yields and have serious economic consequences. The produced mycotoxins have harmful effects on human and animal health. The aim of this study was to develop classification models based on selected textural parameters to distinguish between infected and healthy wheat kernels. The classification accuracy of kernels positioned on the ventral side was determined at 78–100% in the model based on textural parameters from hyperspectral images, and at 95–100% based on images generated by a flatbed scanner. Kernels positioned on the dorsal side were correctly classified in 78–98% based on hyperspectral images, and in 92–100% based on colour images. In the models combining textural parameters from the ventral and dorsal sides of wheat kernels, classification accuracy reached 76–98% in hyperspectral images, and 94–100% in images generated by a flatbed scanner. The imaging technique—flatbed scanner and the ventral side of the kernels provided higher classification accuracy results. The results will contribute to further research aiming to develop models for the determination of fungal chemotypes and/or fungal species based on selected textural features of wheat kernels.