Recognizing Mangoes and Determining their Ripeness Through the Application of Image Processing and Machine Learning Techniques

Authors

  • Ashwini B. Gavali Assistant Professor, Computer Engineering Department, S. B. Patil College of Engineering, Indapur (MH), India
  • Somnath B. Thigale Associate Professor, Computer Science & Engineering (AIDS) Department, Shree Siddheshwar Women's College of Engineering, Solapur (MH), India
  • Pradeep S. Togrikar Assistant Professor, E & TC Engineering Department, S. B. Patil College of Engineering, Indapur (MH), India
  • Swagat M. Karve Assistant Professor, E & TC Engineering Department, S. B. Patil College of Engineering, Indapur (MH), India
  • Swapnaja A. Ubale Associate Professor, Information Technology Department, Marathwada Mitra Mandal College of Engineering, Pune (MH), India
  • Sonali B. Gavali Assistant Professor, AI & DS Department, Dr. D. Y. Patil, Institute of Technology, Pimpri, Pune

Keywords:

Ripeness, Machine Learning, Image Processing, Cost-efficient, Object detection algorithm, Faster R-CNN

Abstract

The fruit market faces challenges in grading, with existing commercial systems being prohibitively expensive. In contrast, smaller businesses often rely on manual grading systems, which are susceptible to human errors and inaccuracies. This research introduces an innovative approach to identify and grade Mango, aligning with the principles of Industry 4.0. Employing a Faster Region-based Convolution Neural Network (Faster R-CNN) object detection algorithm through Tensor Flow, the system efficiently detects the fruit and utilizes image processing to assess the likely percentage of ripeness. This allows for the categorization of the fruit into specific classes. The study demonstrates that the proposed methods are not only effective but also cost-efficient for accurately determining fruit ripeness. Moreover, with effective training, the same system can be adapted for multiple fruits, showcasing its versatility and applicability across various produce.

Downloads

Download data is not yet available.

References

L. Ma, S. Fadillah Umayah, S. Riyadi, C. Damarjati, and N. A. Utama, “Deep Learning Implementation using Convolutional Neural Network in Mangosteen Surface Defect Detection,” 7th IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2017), 24–26 November 2017, Penang, Malaysiano. November, pp. 24–26, 2017.

K. N. Ranjit, H. K. Chethan, and C. Naveena, “Identification and Classification of Fruit Diseases,”Int. Journal of Engineering Research and Application, ISSN- 2248-9622 vol. 6, no. 7, pp. 11–14, 2016.

H. Jang, H. Yang, and D. Jeong, “Object Classification using CNN for Video Traffic Detection System,” International Conference21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV) no. 1, pp. 1–4, 2015.

Jaramillo J., Rodriguez V., Guzman M., Zapata M. and Rengifo T., "Technical Manual: Good Agricultural Practices in the Production of Tomato under Protected Conditions".Food and Agriculture Organization (FAO) 2007.

S. Jana and S. Basak, “Automatic Fruit Recognition from Natural Images using Color and Texture Features,” IEEE Devices Integrated Circuit, pp. 620–624, 2017.

C. S. Nandi, B. Tudu, and C. Koley, “A machine vision-based maturity prediction system for sorting of harvested mangoes,” IEEE Trans. Instrum. Meas., vol. 63, no. 7, pp. 1722–1730, 2014.

S. Basak, “An Improved Bag-of-Features Approach for Object Recognition from Natural Images,” Int. Journal Computer Appl. (0975 – 8887), vol. 151, no. 10, pp. 5–11, 2016.

M. Fojlaley, P. A. Moghadam, and A. niaSaeed, “Tomato Classification and Sorting with machine vision using SVM, MLP, and LVQ,” Int. Jouranl. Agriculture Crop Science, vol. 4, no. 15, pp. 1083–1088, 2012.

Rismiyati and S. N. Azhari, “Convolutional Neural Network implementation for image-based Salak sortation,” 2nd International Conferenceon Science and Technology Computer, ICST 2016, pp. 77–82. 2017

S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 1096, pp. 1137–1149, 2015.

https://www.raspberrypi.org/products/raspberry-pi-3-model-b-plus/

Bengio, Y., Goodfellow, I. danCourville, A. “Convolutional Networks, In Deep Learning,” Book in preparation for MIT Press, pp. 199–216,2015.

A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications,” ArXiv,pg.1-9,Apr. 2017.

P. Goldsborough, “A Tour of Tensor Flow,” ArXiv,pg.1-12,Oct. 2016.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” Arxiv.Org, pg.1-12,Dec.2015.

Downloads

Published

24.03.2024

How to Cite

Gavali, A. B. ., Thigale, S. B. ., Togrikar , P. S. ., Karve, S. M. ., Ubale, S. A. ., & Gavali, S. B. . (2024). Recognizing Mangoes and Determining their Ripeness Through the Application of Image Processing and Machine Learning Techniques. International Journal of Intelligent Systems and Applications in Engineering, 12(19s), 624–629. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5106

Issue

Vol. 12 No. 19s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.