Discrimination of wheat grain varieties using image analysis and neural networks. Part I. Single kernel texture

The development of non-destructive methods for the evaluation of cereal grain varieties has important implications for the food processing industry. The described experiment investigated 11 varieties of spring and winter wheat of different quality class. The analysis was performed on images acquired from a flatbed scanner interfaced to a PC. Kernel images were digitalized at high resolution (2673 × 4031) with 24-bit depth and 400 dpi. The variables input into the statistical model were the textures of single kernel projections. Textures were computed separately for seven channels (R, G, B, Y, S, U, V). The results were examined with the application of discriminant analysis and neural networks. The accuracy of texture-based classification of 11 wheat varieties reached 100%. The experimental design which yielded the most satisfactory results comprised texture measurements from the combined area of 20 kernels and variables from seven channels input into the neural network. The final classification quality was not affected by the year of cultivation, moisture content or grain variety.

► The accuracy of texture-based classification of 11 wheat varieties reached 100%.
► The ultimate model contained only 21 variables from the seven analyzed channels.
► The cultivation year, grain moisture content did not affect classification quality.
► The proposed method is easy to use and effective.