CFP last date
22 December 2025
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 22 December 2025

Submit your paper
Know more
The week's pick
Responsive image
A Hybrid Transformer-CNN Framework with Early and Late Fusion for Robust Skin Lesion Classification

Raihan Tanvir
Random Articles
Reseach Article

Classification vs. Regression for Real-Time Fruit Shelf-Life Prediction: A Transfer Learning Approach with MobileNetV2 for Edge AI

by Sai Sri Chandana Jada, M. Senthil, Kolla Vivek
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 53
Year of Publication: 2025
Authors: Sai Sri Chandana Jada, M. Senthil, Kolla Vivek
10.5120/ijca2025925908

Sai Sri Chandana Jada, M. Senthil, Kolla Vivek . Classification vs. Regression for Real-Time Fruit Shelf-Life Prediction: A Transfer Learning Approach with MobileNetV2 for Edge AI. International Journal of Computer Applications. 187, 53 ( Nov 2025), 50-57. DOI=10.5120/ijca2025925908

@article{ 10.5120/ijca2025925908,
author = { Sai Sri Chandana Jada, M. Senthil, Kolla Vivek },
title = { Classification vs. Regression for Real-Time Fruit Shelf-Life Prediction: A Transfer Learning Approach with MobileNetV2 for Edge AI },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2025 },
volume = { 187 },
number = { 53 },
month = { Nov },
year = { 2025 },
issn = { 0975-8887 },
pages = { 50-57 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number53/classification-vs-regression-for-real-time-fruit-shelf-life-prediction-a-transfer-learning-approach-with-mobilenetv2-for-edge-ai/ },
doi = { 10.5120/ijca2025925908 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-11-18T21:10:40.409078+05:30
%A Sai Sri Chandana Jada
%A M. Senthil
%A Kolla Vivek
%T Classification vs. Regression for Real-Time Fruit Shelf-Life Prediction: A Transfer Learning Approach with MobileNetV2 for Edge AI
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 53
%P 50-57
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The agricultural industry faces significant challenges in managing perishable goods, with a substantial portion of produce being wasted due to spoilage. This paper presents a comparative study of two deep learning approaches for predicting the shelf life of fruits using Convolutional Neural Networks (CNNs) and edge computing. This study developed and evaluated both a classification model and a regression model, both based on the MobileNetV2 architecture for a fair comparison. The classification model achieved a test accuracy of 74.75%, while the regression model provided more granular predictions with a mean absolute error of 1.44 days. Both models were converted to the TensorFlow Lite (TFLite) format and evaluated on the test set, achieving identical performance to their Keras counterparts while significantly reducing prediction latency. This research explores the advantages and disadvantages of both classification and regression approaches, demonstrating the potential of deep learning and edge computing to create scalable and efficient solutions for real-time shelf-life prediction, which can help to reduce food waste and optimize supply chain management.

References
  1. L. Liu, P. Ouyang, W. Wang, F. Li, and D. Chen, "A review of fruit ripeness detection using computer vision," Journal of Food Engineering, vol. 283, p. 110030, 2020.
  2. A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," in Advances in Neural Information Processing Systems 25 (NIPS 2012), 2012, pp. 1097-1105.
  3. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-Based Learning Applied to Document Recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.
  4. A. Bhargava and A. Bansal, “Fruit recognition using convolutional neural networks,” in 2018 8th International Conference on Cloud Computing, Data Science & Engineering (Confluence), 2018, pp. 1-5.
  5. S. S. Pan and Y. C. Wang, "A deep learning approach for fruit quality classification," in 2019 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), 2019, pp. 1-2.
  6. M. Satyanarayanan, "The Emergence of Edge Computing," Computer, vol. 50, no. 1, pp. 30-39, 2017.
  7. M. Z. Uddin, M. T. I. Khan, and M. A. Iqbal, "An IoT-based automated system for monitoring of fruit freshness," in 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), 2019, pp. 1-6.
  8. M. A. Wazzan, S. A. Al-Ghamdi, and A. M. Al-Ghamdi, "A Real-Time Fruit Quality Classification System Using Deep Learning," Arabian Journal for Science and Engineering, vol. 46, pp. 11845–11861, 2021.
  9. Y. Safari, J. Nakatumba-Nabende, R. Nakasi, and R. Nakibuule, "A Review on Automated Detection and Assessment of Fruit Damage Using Machine Learning," International Journal of Computer Applications, vol. 184, no. 1, pp. 1-10, 2022.
  10. S. S. Shenoy, R. Mishra, and R. Chaturvedi, "Comparative Analysis for Predicting Shelf life of Fruits Using Advanced Deep Learning Approaches," in 2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC), 2022, pp. 586-591.
  11. S. S. Alahmari and T. Salem, "Food State Recognition Using Deep Learning," IEEE Access, vol. 8, pp. 195515-195525, 2020.
  12. A. Abougharib, M. Awad, and M. Ndiaye, "Remaining Shelf-Life Estimation of Fresh Fruits and Vegetables During Transportation," IEEE Access, vol. 8, pp. 175586-175599, 2020.
  13. A. G. Howard et al., "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications," arXiv preprint arXiv:1704.04861, 2017.
  14. K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770-778.
  15. K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," in International Conference on Learning Representations (ICLR), 2015.
  16. Abhinav099802, et al. 2023. Time-fruits Market Availability. Data set. Kaggle. https://www.kaggle.com/datasets/abhinav099802/time-fruits-market-availability.
  17. F. Chollet, "Keras," GitHub, 2015. [Online]. Available: https://github.com/fchollet/keras.
  18. G. Bradski, "The OpenCV Library," Dr. Dobb's Journal of Software Tools, 2000.
  19. P. Wes McKinney, "Data Structures for Statistical Computing in Python," in Proceedings of the 9th Python in Science Conference, 2010, pp. 51-56.
  20. H. Zhu and X. Li, "Deep Learning Based Food Category Classification and Recognition," in 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), 2018, pp. 634-638.
  21. J. D. Hunter, "Matplotlib: A 2D Graphics Environment," Computing in Science & Engineering, vol. 9, no. 3, pp. 90-95, 2007.
  22. W. Shi, J. Caballero, F. Huszár, J. Totz, A. P. Aitken, R. Bishop, D. Rueckert, and Z. Wang, "Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 1874-1883.
  23. M. A. Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K., "State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow's Intelligent Network Traffic Control Systems," IEEE Communications Surveys & Tutorials, vol. 19, no. 4, pp. 2432-2455, 2017.
  24. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 779-778.
  25. S. P. Mohanty, D. P. Hughes, and M. Salathé, "Using Deep Learning for Image-Based Plant Disease Detection," Frontiers in Plant Science, vol. 7, p. 1419, 2016.
  26. R. Girshick, "Fast R-CNN," in 2015 IEEE International Conference on Computer Vision (ICCV), 2015, pp. 1440-1448.
  27. J. Long, E. Shelhamer, and T. Darrell, "Fully Convolutional Networks for Semantic Segmentation," in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 3431-3440.
  28. S. Ren, K. He, R. Girshick, and J. Sun, "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks," in Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015, pp. 91-99.
  29. V. B. S. Prasath, H. A. F. Al-Dmour, A. H. A. El-Kader, and S. A. H. Al-Hassan, "Fruit image classification using deep learning," in 2017 8th International Conference on Information Technology (ICIT), 2017, pp. 810-815.
  30. S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997.
  31. M. Abadi et al., "TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems," arXiv preprint arXiv:1603.04467, 2016.
Index Terms

Computer Science
Information Sciences

Keywords

Shelf-Life Prediction Fruit Spoilage Convolutional Neural Networks (CNN) Edge Computing TensorFlow Lite (TFLite) Image Classification Computer Vision Deep Learning Food Waste Supply Chain Management Banana Ripeness

Powered by PhDFocusTM