International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | A Deep Learning and Transfer Learning-Based Model for Fruit Classification |
|---|---|
| ABSTRACT | This paper explores the application of advanced Convolutional Neural Networks (CNNs) for automated fruit classification, focusing on models such as Inception-V3, VGG-19, MobileNet, ResNet-50, and a traditional CNN. The dataset used for this research, sourced from Kaggle, consists of images representing five types of fruits: Apples, Bananas, Grapes, Mangoes, and Strawberries. Each model's architecture was carefully selected for its ability to extract complex visual features and perform accurate classification, addressing challenges associated with variations in fruit appearance, color, and texture. The evaluation process involved training and testing the models to determine their effectiveness in real-world classification tasks. By leveraging convolutional layers, residual connections, and efficient network designs, the models were assessed based on their generalization capabilities and computational efficiency. This research provides insights into the advantages and limitations of different CNN architectures, contributing to the development of automated systems for fruit recognition. Such systems can be applied in agricultural quality control, retail inventory management, and automated sorting processes, offering significant advancements in food industry automation. |
| AUTHOR | M. SRI ARCHANA, DR. M. KRISHNA |
| PUBLICATION DATE | 2025-10-18 |
| VOLUME | 175 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1310019 |
| pdf/19_A Deep Learning and Transfer Learning-Based Model for Fruit Classification.pdf | |
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