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

作     者：Ratha, Ashoka Kumar Barpanda, Nalini Kanta Sethy, Prabira Kumar Behera, Santi Kumari 

作者机构：Sambalpur Univ Dept Elect Engn Jyoti Vihar Sambalpur 768019 Odisha India Guru Ghasidas Vishwavidyalaya Dept Elect & Commun Engn Bilaspur 495009 Chhattisgarh India VSSUT Dept Comp Sci & Engn Sambalpur 768018 Odisha India 

出 版 物：《TRAITEMENT DU SIGNAL》 (Trait. Signal)

年 卷 期：2024年第41卷第2期

页      面：669-678页

核心收录：

学科分类：0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：mango identification deep learning convolutional neural network (CNN) computer vision machine learning Support Vector Machine (SVM) 

摘      要：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.