On Tree Mango Fruit Detection and Counting System


Romil Mahajan
Ambarish Haridas
Mohit Chandak
Rudar Sharma
Charanjeet Dadiyala


For yield estimation, it is crucial to achieve quick and precise identification of mango fruits in the natural situations and surroundings. Using imaging with computer vision to accurately detect and count fruits during plant growth is important. It is not just because it is a vital step toward automating procedures like harvesting but also for minimizing labour-intensive human assessments of phenotypic information which can be useful for the farmer. Fruit farmers or cultivators in agriculture would benefit greatly from being able to track and predict production prior to fruit harvest. In order to make the best use of the resources needed for each individual site, such as water use, fertiliser use, and other agricultural chemical compounds. Mango fruit is considered in this paper. A comparative study on Faster R-CNN, YOLOv3 algorithms, and YOLOv4 algorithms, which are widely used in the field of object recognition in the past on various fruits and objects, was conducted to find the best model. The YOLOv4 algorithm was chosen as it was the best technique for mango fruit recognition based on the findings of the above comparative study. A real-time mango fruit detection method utilizing YOLOv4 deep learning algorithm is put forward. The YOLOv4 (You Only Look Once) model was developed under the CSPDarknet53 framework. Also, the number of mangoes in the image or frame was counted and displayed in images as well as videos.


Author Biographies

Romil Mahajan, Shri Ramdeobaba College of Engineering and Management, Nagpur

Romil Mahajan is a student of Shri Ramdeobaba College of Engineering and Manage-
ment, and is currently pursuing his bachelor’s degree in Computer Science and Engi-
neering. He is fascinated by Technology and the leverage that Technology is bringing to
humans’ lives. He is passionate to become a part of the community which is at the pro-
ducing end of the technology products and services. His research and technical interests
include Deep Learning, Machine Learning, Cloud Computing and Data Science. He is
Content Lead at Google Developers Student Club, RCOEM. He has been highly involved
in Technology and Content Creation has been part of startups like Food for U and O(1)
Coding Club as a contributor and volunteer.

Ambarish Haridas, Shri Ramdeobaba College of Engineering and Management, Nagpur

Ambarish Haridas is a final year B.E. computer science and engineering student in Shri Ramdeobaba College of Engineering and Management, Nagpur. He is interested in Machine Learning, Deep Learning and Web Development. He has attended workshops to understand and apply the machine learning concepts on projects. He is keen to work on an impactful project powered by machine learning/deep learning in future. He has participated in Smart India Hackathon and got shortlisted in college level round.

Mohit Chandak, Shri Ramdeobaba College of Engineering and Management, Nagpur

Mohit Chandak is a Computer Science and Engineering final year student from Shri
Ramdeobaba College of Engineering and Management, Nagpur. He is a finalist in Toy-
cathon 2021 and has been a lead Community Manager for GDG Cloud, Nagpur for years.
He is also a core team member of GyaaniBuddy(A website for students by students).
He has secured the first position in Skillvalley Web Development cohort. He is eager to
explore new opportunities towards building new communities

Rudar Sharma, Shri Ramdeobaba College of Engineering and Management, Nagpur

Rudar Sharma is a final year student in Shri Ramdeobaba College of Engineering and
Management, Nagpur, doing his B.E. in Computer Science and Engineering. He is open
and eager to learn and explore opportunities.

Charanjeet Dadiyala, Shri Ramdeobaba College of Engineering and Management, Nagpur

Mr. Charanjeet Dadiyala is an Assistant Professor in the Department of Computer
Science Engineering, Shri Ramdeobaba College of Engineering and Management, Nag-
pur. He is currently pursuing Ph.D. in Computer Science and Engineering from Kalinga
University, Naya Raipur in the area of Multivariate Multi sourced Prediction Model and
its accuracy improvement using Machine Learning and Deep Learning techniques. He has
completed his M.Tech. in Computer Science and Engineering from Rashtrasant Tukdoji
Maharaj Nagpur University, Nagpur. He has more than 13 years of teaching experience.
He has published more than 14 research papers in reputed International Journals and
Conferences. He has also 1 Indian Patent and 1 Indian Copyright in his credit. He was
a University Rank Holder in Graduation and Post Graduation. He has the membership
of IEEE CSI. His research interests are in Machine Learning, Artificial Intelligence and
Web Technologies.

How to Cite
Romil Mahajan, Ambarish Haridas, Mohit Chandak, Rudar Sharma, & Charanjeet Dadiyala. (2023). On Tree Mango Fruit Detection and Counting System. International Journal of Next-Generation Computing, 14(1). https://doi.org/10.47164/ijngc.v14i1.1022


  1. Bargoti, S. and Underwood, J. 2016. Deep fruit detection in orchards. arXiv preprint arXiv:1610.03677 .
  2. Bargoti, S. and Underwood, J. 2017. Deep fruit detection in orchards. In 2017 IEEE international conference on robotics and automation (ICRA). IEEE, 3626–3633. DOI: https://doi.org/10.1109/ICRA.2017.7989417
  3. Bochkovskiy, A., Wang, C.-Y., and Liao, H.-Y. M. 2020. Yolov4: Optimal speed and accuracy of object detection.
  4. Hutagalung, A., Nugroho, H., Suheryadi, A., and Yunanto, P. E. 2017. Detection and counting of mango fruits in occluded condition using image analysis. In 2017 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME). IEEE, 190–195. DOI: https://doi.org/10.1109/ICICI-BME.2017.8537729
  5. Koirala, A., Walsh, K. B., Wang, Z., and Anderson, N. 2020. Deep learning for mango (mangifera indica) panicle stage classification. Agronomy 10, 1, 143. DOI: https://doi.org/10.3390/agronomy10010143
  6. Kumar, A., Kalia, A., and Sharma, A. 2020. Object detection: A comprehensive review of the state-of-the-art methods. International Journal of Next-Generation Computing, 52–75.
  7. Lawal, M. O. 2021. Tomato detection based on modified yolov3 framework. Scientific Re-ports 11, 1, 1–11. DOI: https://doi.org/10.1038/s41598-021-81216-5
  8. Li, Y., Lei, C., Xue, Z., Zheng, Z., and Long, Y. 2021. A comparison of yolo family for apple detection and counting in orchards. International Journal of Computer and Systems Engineering 15, 5, 334–343.
  9. Parico, A. I. B. and Ahamed, T. 2021. Real time pear fruit detection and counting using yolov4 models and deep sort. Sensors 21, 14, 4803. DOI: https://doi.org/10.3390/s21144803
  10. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. 2016. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition. 779–788. DOI: https://doi.org/10.1109/CVPR.2016.91
  11. Srivastava, S., Divekar, A. V., Anilkumar, C., Naik, I., Kulkarni, V., and Pattabira-man, V. 2021. Comparative analysis of deep learning image detection algorithms. Journal of Big Data 8, 1, 1–27. DOI: https://doi.org/10.1186/s40537-021-00434-w
  12. Syal, A., Garg, D., and Sharma, S. 2014. Apple fruit detection and counting using computer vision techniques. In 2014 IEEE International Conference on Computational Intelligence and Computing Research. IEEE, 1–6. DOI: https://doi.org/10.1109/ICCIC.2014.7238364
  13. Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y. M. 2021. Scaled-YOLOv4: Scaling cross stage partial network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 13029–13038. DOI: https://doi.org/10.1109/CVPR46437.2021.01283
  14. Wu, J. 2018. Complexity and accuracy analysis of common artificial neural networks on pedes-trian detection. In MATEC Web of Conferences. Vol. 232. EDP Sciences, 01003. DOI: https://doi.org/10.1051/matecconf/201823201003