Paper Title
Design and Implementation of IOT based Rodent Monitoring and Avoidance System in Large Scale Grain Storages

This paper presents image classification to identify rodent in large scale storage of grains such as in go-downs or in warehouses. The purpose of the classification of images in storages so as to find appropriate features that can identify rodent monitoring. A combination of several features is used to evaluate the appropriate features to find distinctive features for identification of rodent in storages. This paper presents an application of gray level co-occurrence matrix (GLCM) to extract second order statistical texture features for motion estimation of images. The results are shown in MATLAB and these texture features have high discrimination accuracy, requires less computation time and hence efficiently used for real time Pattern Recognition applications. Keywords - GLCM, Feature Extraction, IDM, Angular Second Moment