Image Processing for Classifying the Quality of the Chok-Anan Mango by Simulating the Human Vision using Deep Learning

Main Article Content

Nopparut Pattansarn
Nattavut Sriwiboon


This paper uses image processing technology with the deep learning methods, which can simulate the human vision to develop a model for examining and classifying the quality of the Chok-Anan mango. The research method, we have collect the image of Chok-Anan mangos and collecting quality classification data, determining quality levels into 4 levels consisting of grade A, B, C and grade D are rotten mangos. The results of the research shown that the use of deep learning by the convolutional neural network (CNN) algorithm for image processing to create a model showing the excellent accuracy at 99.79%. Then, we use the model to develop as a prototype for image classifying of Chok-Anan mangos. The result has found that the success rate of classification at 100%.


Download data is not yet available.

Article Details

How to Cite
N. Pattansarn and N. Sriwiboon, “Image Processing for Classifying the Quality of the Chok-Anan Mango by Simulating the Human Vision using Deep Learning”, JIST, vol. 10, no. 1, pp. 24-29, Jun. 2020.
Research Article: Multidisciplinary (Detail in Scope of Journal)


[1] J. Jaroenjit, A. Panpanasakul, P. Chaisri, P. Promduang, and S. Prompongusawa, "Classification pearls using image processing," in Proceedings of the 9th Hatyai National and International Conference, Thailand, 2014, pp. 1679 - 1691.

[2] A. Tungkastan and K. Leewun, "Pixel-Based Car Model Detection and Recognition," Engineering Journal of Siam University, vol. 19, January-June, pp. 90–102, 2018.

[3] S. Phimphisan, "Application of Data Mining for Diabetic Retinopathy Using Decision Tree," Journal of Srivanalai Vijai, vol. 3, 2016.

[4] S. Sarraf and G. Tofighi, "A hybrid sequential feature selection approach for the diagnosis of Alzheimer's Disease," in Proceedings of International Joint Conference on Neural Networks, 2016, pp. 1216-1220.

[5] E. Humphrey and J. Bello, "Rethinking Automatic Chord Recognition with Convolution Neural Networks," in Proceedings of the 11th International Conference on Machine Learning and Application, 2012.

[6] T. Tathawee, S. Prasarnpun, S. Onbua, T. Pinthong, and A. Suwannakom, "Orchid identification based on computer vision analysis," in Proceedings of the 6th National Science Research Conference, Thailand, 2014, pp. 47 - 56.

[7] B. Tilmann, "The Business Impact of Predictive Analytics," ed. IGI Global, September - December 2007.

[8] R. Kohavi, "A study of crossvalidation and bootstrap for accuracy estimation and model selection," in Proceedings of the Fourteenth International joint conference on Artificial Intelligence, Montreal, Canada 1995, pp. 1137-1143.

[9] A. Paisal and T. Kasetkasem, "Separation the mingling varieties of the mungbean seeds by image processing," Khon Kaen Agriculture Journal, vol. 3, pp. 240-247, 2011.

[10] N. Masunee, "Development of an image processing system in splendid squid quality classification," Prince of Songkla University, 2014.

[11] S. Aunkaew, T. Khaorapapong, and M. Karnjanadecha, "A Study of Inspection of White Mould on Surface Rubber Sheets Using Image Processing," Thaksin University Journal, vol. 10, July - December, pp. 50–61, 2007.

[12] A. Rosebrock, "Installing Keras with TensorFlow backend," January, 2020. [Online]. Available: [Accessed: Jan 7, 2020].