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Original Article
Fruit Damage Detection: An Automated Approach for Quality Control in Food Industry
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.
Published Online: July-August 2025
Pages: 43-48
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20250504008
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
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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