Tata Consultancy Services Limited

Nagameena Neelakantapillai

Srinivasu Pappula

Sanat Sarangi

Vaibhav Sadashiv Lonkar

Dinesh Kumar Singh

Karthik Srinivasan

Swagatam Bose Choudhury

Rajatkumar Shrivastav

State of art techniques mostly rely of computationally intensive, time consuming Neural Networks. Embodiments provide a method and system for identification and classification of different grain and adulterant types for grain grading analysis. The method analyzes input image of grain sample of elements to determine morphological features of elements, using dynamically determined calibration factor from reference object in the image. Variation in perimeter of elements is used to perform classification of elements into target grain size, low size adulterants and higher size adulterants. The aspect ratio of target grain determines grain variety and adulterants determine adulteration percentage. Elements are classified into grain colored and non-grain colored adulterants. Grain colored adulterants are further classified as Grain Like Impurities and non-GLI, using predefined ranges of standard deviation of perimeter metric. Weight of grain colored adulterants and non-grain colored adulterant is obtained using mapping of predefined weights to the aspect ratio.

Method and system for identification and classification of different grain and adulterant types

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Chennai, India

Nagameena Neelakantapillai