Agriculture industry is the backbone of any emerging country, in developing countries like India the bulk of the rural population mostly depend on agriculture as their main basis of revenue. Several major crops grown in India are rice, wheat, maize, jowar etc. The primary focus of this paper is to present an efficient method to detect quality of rice grains and also to detect the dissimilar varieties of rice grains via various image processing methodologies. Commercialization of rice grains is mainly dependent on the quality analysis of rice grains which is further based on size of the grain kernel (full, half or broken) and rice varieties are recognized by human inspection. Results of the inspections may change depending on the expertise of inspectors and decisions made by inspectors according to external parameters. Furthermore, the decision making abilities of quality check inspectors are dependent on their personal characteristics such as exhaustion, annoyance, partiality etc. and identification of dissimilar varieties is carried out by applying traditional methods which are erroneous and consumes more time. Digital image processing techniques will help overcome the above issues, which offers quick, economical, and reliable solutions.

Agriculture, Grain Kernel, Image processing, Feature extraction, Grain counting, classification of rice grains

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