Bit plane slicing technique to classify date varieties

Abstract

Varietal purity is an important parameter in the quality standards of dates. In general, variety identification is done by visual inspection method in grading and handling facilities. Online variety assessment using computer vision methods with minimum features and fast image processing and classification algorithms would be highly beneficial for the date industry. Three date varieties (Khalas, Fard and Madina) were classified using a single type of feature, Euler number, used on the eight bit planes available from gray scale images. An overall classification accuracy of 91.5% was achieved using a two layer neural network classifier with hyperbolic tangent sigmoid transfer function. Additionally, image segmentation was performed using the two most significant bit planes. Therefore, a complete feature extraction module based on logic values and morphological image processing as proposed here can be easily implemented in hardware.