Automated Classification of Indian Mango Varieties Using Machine Learning and MobileNet-v2 Deep Features

Abstract

In agricultural applications, the utilization of image processing with machine learning, particularly for fruit classification, has become increasingly prevalent. This study focuses on the automated classification of various Indian mango varieties, employing the deep features of MobileNet-v2 and Shufflenet, integrated with diverse machine learning classifiers. The research is anchored on an extensive dataset, encompassing 15 distinct Indian mango varieties, meticulously collated from various vegetable markets across India. This dataset is accessible at "Sethy, Prabira Kumar; Behera, Santi; Pandey, Chanki (2023), 'Mango Variety', Mendeley Data, V2, doi: 10.17632/tk6d98f87d.2". A comprehensive comparison of various machine learning classifiers highlighted the dominance of the Cubic Support Vector Machine (SVM) when integrated with deep features extracted from MobileNet-v2. This pairing resulted in an outstanding classification accuracy of 99.5% and an Area Under the Curve (AUC) of 1, demonstrating exceptional performance in identifying fruit varieties. The significance of this research lies in its potential to revolutionize fruit classification processes in supermarkets and related sectors. By demonstrating the feasibility of applying advanced computer vision technology for the accurate classification of fruits, this study lays the groundwork for future exploration into the scalability, robustness, and wider applicability of these methods, potentially extending beyond mangoes to other fruit varieties. Such advancements could substantially benefit the agricultural industry, enhancing efficiency in both production and retail sectors.