Implementation of ML Algorithm for Mung Bean Classification using Smart Phone
Muhammad Shahid, Kashif Munir, Salman Muneer, Mutiullah, Muath Jarrah, Umer Farooq
Abstract
This work is an extension of my work presented a robust and economically efficient method for the discrimination of four Mung-Beans varieties based on quantitative parameters, Due to the advancement of technology day by day users try to find the solutions to their daily life problems using smartphones but still there is limited resources are available in smartphone concerning computing power and memory so there is need to find the best classifier which can classify the Mung-Beans using already suggested features in previous work with minimum memory requirements and computational power. For achieving the goal of this study, we take the experiments on various supervised classifiers which have simple architecture and calculations and give the robust performance on the most relevant 10 suggested features are selected by Fisher Co-efficient, Probability of Error, Mutual Information, and wavelet features. After the analysis, we replace the Artificial Neural Network and Deep learning with such a classifier which gives approximately the same classification results as the above classifier but is efficient in terms of resources and time complexity. This classifier is easily implemented in the smartphone environment.