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

Abstract

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%.

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How to Cite
[1]
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.
Section
Research Article: Multidisciplinary (Detail in Scope of Journal)

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