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Original Article
Fruit and Vegetable Image Recognition Using Convolutional Neural Networks
Shaik Mushraf1
Dr. Mohd Rafi Ahmed2
1 Student, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India. 2Associate Professor, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India.
Published Online: September-October 2025
Pages: 13-18
Cite this article
↗ https://www.doi.org/10.59256/ijire.20250605003
References
1. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Advances in Neural Information Processing Systems, vol. 25, pp. 1097–1105, 2012.
2. F. Chollet, “Xception: Deep Learning with Depthwise Separable Convolutions,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1251–1258.
3. J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” arXiv preprint arXiv:1804.02767, 2018.
4. A. Mureșan and M. Oltean, “Fruit Recognition from Images Using Deep Learning,” Acta Universitatis Sapientiae, Informatica, vol. 10, no. 1, pp. 26–42, 2018.
5. S. Kamilaris and F. X. Prenafeta-Boldú, “Deep learning in agriculture: A survey,” Computers and Electronics in Agriculture, vol. 147, pp. 70–90, Apr. 2018.
6. H. Hasan, R. Abdul Rahman, and M. H. Marhaban, “Classification of fruits using convolutional neural network,” in Proc. IEEE Int. Conf. on Control System, Computing and Engineering (ICCSCE), 2018, pp. 242–247.
7. M. R. Khan et al., “Smart Farming Using Image Processing and Machine Learning,” IEEE Access, vol. 7, pp. 170645–170661, 2019.
8. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, May 2015.
9. K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv preprint arXiv:1409.1556, 2014.
10. A. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” Journal of Big Data, vol. 6, no. 1, pp. 1–48, Jul. 2019.
2. F. Chollet, “Xception: Deep Learning with Depthwise Separable Convolutions,” in Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1251–1258.
3. J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement,” arXiv preprint arXiv:1804.02767, 2018.
4. A. Mureșan and M. Oltean, “Fruit Recognition from Images Using Deep Learning,” Acta Universitatis Sapientiae, Informatica, vol. 10, no. 1, pp. 26–42, 2018.
5. S. Kamilaris and F. X. Prenafeta-Boldú, “Deep learning in agriculture: A survey,” Computers and Electronics in Agriculture, vol. 147, pp. 70–90, Apr. 2018.
6. H. Hasan, R. Abdul Rahman, and M. H. Marhaban, “Classification of fruits using convolutional neural network,” in Proc. IEEE Int. Conf. on Control System, Computing and Engineering (ICCSCE), 2018, pp. 242–247.
7. M. R. Khan et al., “Smart Farming Using Image Processing and Machine Learning,” IEEE Access, vol. 7, pp. 170645–170661, 2019.
8. Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, May 2015.
9. K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” arXiv preprint arXiv:1409.1556, 2014.
10. A. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep learning,” Journal of Big Data, vol. 6, no. 1, pp. 1–48, Jul. 2019.
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