A Web-based Image Recognition System for Detecting Harumanis Mangoes

Authors

  • Mohamad Shahmil Saari Universiti Teknologi MARA, Perlis Branch, Arau Campus
  • Romiza Md Nor Universiti Teknologi MARA, Perlis Branch, Arau Campus
  • Huzaifah A Hamid Universiti Teknologi MARA, Perlis Branch, Arau Campus

DOI:

https://doi.org/10.24191/jcrinn.v5i4.153

Keywords:

image recognition, fruit texture, convolutional neural networks

Abstract

Harumanis mango cultivar is special to Perlis (north state of Malaysia) and has been declared in the national agenda as a special fruit. For those who are not acquainted with aromatic mango, it is difficult to tell the distinction between Harumanis and the others . By using image recognition, people can identify Harumanis feature details by image recognition technique where algorithm is applied to recognize the mango. Convolutional neural networks method is a suitable technique for the creation of a multi - fruit in re al - time classification sorter with the camera and for the detection of moving fruit. Furthermore, the accuracy of the image classification can be improved by increasing the number of datasets, the distance of images from the camera, and the labelling proce ss. This project used Mobile Net architecture model because it consumes less computational power and it can also provide efficiency of the accuracy. A w eb - based i mage r ecognition s ystem for d etecting Harumanis m angoes was developed and known as CamPauh to recognize four classes of mango which are H arumanis, apple mango, other type s of mango es and not mango. CamPauh ca n identify different type of mangoes and the result was stored into the database and appeared on the websit e. E valuation on the accuracy was conducted discussed to support users satisfaction in identifying the correct mango type.

Downloads

Download data is not yet available.

References

Arivazhagan. (2010). Fruit Recognition using Color and Texture Features. Journal of Emerging Trends in Computing and Information Sciences, (October), 1–5.

Basri, R., Jacobs, D., Kasten, Y. and Kritchman, S. (2019). The convergence rate of neural networks for learned functions of different frequencies. In Advances in Neural Information Processing Systems.

Farook, R. S. M., Ali, H., Harun, A., Ndzi, D. L., Shakaff, A. Y. M., Nor Jaafar, M., Aziz, A. H. A. (2013). Harumanis Mango Flowering Stem Prediction using Machine Learning Techniques. Research Notes in Information Science (RNIS), 13(May), 46–51. https://doi.org/10.4156/rnis.vol13.10

Gupta S. (2018). Understanding Image Recognition and Its Uses. Retrieved from https://www.einfochips.com/blog/understanding-image-recognition-and-its-uses/

Jalled, F., & Voronkov, I. (2016). Object Detection using Image Processing, 1–6. Retrieved from http://arxiv.org/abs/1611.07791

Mustakim Ramli. (2017, April 11). Fake 'Harumanis': Perlis to work with Domestic Trade Ministry to Monitor Traders. Retrieved from https://www.nst.com.my/news/nation/2017/04/229549/fake-harumanis-perlis-work-domestic-trade-ministry-monitor-traders

Singh, R. (2019, June 10). Computer Vision? An Introduction. Retrieved from https://towardsdatascience.com/computer-vision-an-introduction-bbc81743a2f7

Downloads

Published

2020-10-25

How to Cite

Saari, M. S., Md Nor, R., & A Hamid, H. (2020). A Web-based Image Recognition System for Detecting Harumanis Mangoes . Journal of Computing Research and Innovation, 5(4), 48–53. https://doi.org/10.24191/jcrinn.v5i4.153

Issue

Section

General Computing