Tata Consultancy Services Limited

Karthik Srinivasan

Srinivasu Pappula

Sanat Sarangi

Vaibhav Sadashiv Lonkar

Dinesh Kumar Singh

Jayantrao Mohite

Suryakant Ashok Sawant

Saibhargavi Gurram

Nagameena Neelakantapillai

Shubhadra Varma Pusapati

Swagatam Bose Choudhury

Rajatkumar Shrivastav

Shubhadra Varma Pasapati

Monitoring of animals in a herd has various challenges. A method and a system for monitoring and measuring the behavior of a plurality of animals in a herd chamber is provided. The disclosure provides a method to develop a model to identify the animal interactions between a given set of herds of two or more animals. The system uses image, RFID sensors, animal phenotyping information such as species, breed, age and reproduction cycle information to generate an animal profile. This profile is used to quantify the interaction into an aggression score to the animals in the given herd. The aggression score over a given duration can help herd manager to put the animals with similar aggression score together. This will also help analyze the temporal aspects of the aggression score for a given animal and take preventive measures.

Method and system for monitoring and measuring the behavior of a plurality of animals

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

Karthik Srinivasan