Cereal varieties classification using wavelet techniques combined to multi-layer neural networks

This paper presents a new classification method of the various cereal grains varieties. The first phase consists in generating primitives using the wavelet techniques. These primitives are tested by a statistical study and validation tests to extract the deterministic parameters. The second part consists in developing a neuronal classifier designed using the multilayer neural networks to classify the three grain classes (hard wheat, tender wheat and barley). The third part consists to identify the mitadin grains from hard wheat and to classify them in three categories of mitadinage.