Abstract: Digital image analysis was used to develop a pattern recognition algorithm to classify individual kernels of seven Chinese spring wheat cultivars grown at 4 locations. Totally, 20 morphological parameters and 12 color parameters were extracted. Three hundred kernels per sample were used as the training data set to develop identification model, and another 200 kernels were used as the test set. For the test set, the classification accuracy of wheat cultivars was 100% in each growing location. Except for Xinkehan 9 with 98.3%, the correct discrimination of the training set of collective samples is 100% for wheat cultivar. For the test set, the correct discrimination of Longmai 26 and Qingchun 566 were 97.5% and 95.0%, the others is 100%. For the origin of wheat grains, the classification of Gansu, Ningxia, Xinjiang and Heilongjiang were 88.6%, 92.9%, 72.9% and 95.7%, respectively. The results show that it is feasible to identify and classify wheat cultivar (grains) using digital image analysis.

Key words: Common wheat, Variety, Image process, Pattern recognition