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Implementation of ML Algorithm for Mung Bean Classification using Smart Phone


Mubarak Almutairi, Mutiullah, Kashif Munir, and Shadab Alam Hashmi


Vol. 21  No. 11  pp. 89-96


This work is an extension of my work presented a robust and economically efficient method for the Discrimination of four Mung-Beans [1] varieties based on quantitative parameters. Due to the advancement of technology, users try to find the solutions to their daily life problems using smartphones but still for computing power and memory. Hence, there is a need to find the best classifier to classify the Mung-Beans using already suggested features in previous work with minimum memory requirements and computational power. To achieve this study's goal, we take the experiments on various supervised classifiers with simple architecture and calculations and give the robust performance on the most relevant 10 suggested features 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 a classifier that 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.


Mung-Beans, Textural Features, Fisher's Co-efficient; Linear Discriminant, Artificial Neural Network, Smart Phone.