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Original Article

Fruit Damage Detection: An Automated Approach for Quality Control in Food Industry

  • Paper ID 200
  • Volume 5, Issue 4
  • Pages 43-48
  • Published Online: July-August 2025
  • 6 Views
Open Access Peer Reviewed Crossref DOI

Authors

  • Arshiya Jameel1
  • Dr. Khaja Mahabubullah2

1Student, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India. 2Professor&HOD, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India.

Article Information

Published Online
July-August 2025
Pages
43-48
Article Type
Original Article
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References

1. R. V. V. Krishna and B. S. Reddy, “Detection of Fruit Diseases Using Convolutional Neural Networks,” International Journal of Computer Applications, vol. 178, no. 38, pp. 1–5, 2019.
2. A. Kamilaris and F. Prenafeta-Boldú, “Deep Learning in Agriculture: A Survey,” Computers and Electronics in Agriculture, vol. 147, pp. 70–90, Apr. 2018.
3. M. Brahimi, K. Boukhalfa, and A. Moussaoui, “Deep Learning for Tomato Diseases: Classification and Symptoms Visualization,” Applied Artificial Intelligence, vol. 31, no. 4, pp. 299–315, 2017.
4. P. Ferentinos, “Deep Learning Models for Plant Disease Detection and Diagnosis,” Computers and Electronics in Agriculture, vol. 145, pp. 311–318, Feb. 2018.
5. 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.
6. S. Zhang, X. Wu, and Z. You, “Fruit Detection and Counting Using Deep Learning with Faster R-CNN,” IEEE Access, vol. 8, pp. 90992–91003, 2020.
7. OpenCV Documentation. [Online]. Available: https://docs.opencv.org
8. TensorFlow Documentation. [Online]. Available: https://www.tensorflow.org
9. PyTorch Documentation. [Online]. Available: https://pytorch.org/docs
10. Streamlit Documentation. [Online]. Available: https://docs.streamlit.io
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  • ISSN 2583-0368
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