Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)

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Efficient Fruit Classification Using Tiny YOLO and Neural Networks

Authors
K. Lakshmi Anusha1, Md. Saba Sultana1, *, D. Sahithi1, B. Pradeep1
1Department of Computer Science and Engineering (Data Science), Vignan Institute of Technology and Science, Deshmukhi, Hyderabad, India
*Corresponding author. Email: sabasultana965@gmail.com
Corresponding Author
Md. Saba Sultana
Available Online 4 November 2025.
DOI
10.2991/978-94-6463-858-5_56How to use a DOI?
Keywords
Deep Learning; Fruit Freshness; MobileNetV2; Transfer Learning; Convolutional Neural Networks (CNN); Image Classification; Artificial Intelligence
Abstract

This study presents an AI-driven approach for evaluating fruit freshness by utilizing the MobileNetV2 model. The method aims to replace conventional manual inspections with an automated system for distinguishing between fresh and spoiled fruits. Through transfer learning techniques, the model was trained on a comprehensive dataset comprising over 10,000 images, focusing on three fruit categories: apples, bananas, and oranges. By applying data preprocessing and optimization techniques, the system achieved a test accuracy of 99%, demonstrating its reliability in assessing food quality. This research provides a cost-efficient solution for automating fruit sorting, with future plans to broaden its application by incorporating more fruit types and testing in real-world environments.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
Series
Advances in Computer Science Research
Publication Date
4 November 2025
ISBN
978-94-6463-858-5
ISSN
2352-538X
DOI
10.2991/978-94-6463-858-5_56How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - K. Lakshmi Anusha
AU  - Md. Saba Sultana
AU  - D. Sahithi
AU  - B. Pradeep
PY  - 2025
DA  - 2025/11/04
TI  - Efficient Fruit Classification Using Tiny YOLO and Neural Networks
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 647
EP  - 656
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_56
DO  - 10.2991/978-94-6463-858-5_56
ID  - Anusha2025
ER  -