A comparative study of rice variety classification based on deep learning and hand-crafted features

Main Article Content

Vinh TRUONG HOANG
Duc Phan Van Hoai
Thongchai Surinwarangkoon
Huu-Thanh Duong
Kittikhun Meethongjan

Abstract

Rice is vital to people all around the world. The demand for an efficient method in rice seed variety classification is one of the most essential tasks for quality inspection. Currently, this task is done by technicians based on experience by investigating the similarity of colour, shape and texture of rice. Therefore, we propose to find an appropriate process to develop an automation system for rice recognition. In this paper, several hand-crafted descriptors and Convolutional Neural Networks (CNN) methods are evaluated and compared. The experiment is simulated on the VNRICE dataset on which our method shows a significant result. The highest accuracy obtained is 99.04% by using DenNet21 framework.

Article Details

How to Cite
[1]
V. TRUONG HOANG, D. P. Van Hoai, T. Surinwarangkoon, H.-T. Duong, and K. Meethongjan, “A comparative study of rice variety classification based on deep learning and hand-crafted features”, ECTI-CIT, vol. 14, no. 1, pp. 1-10, Mar. 2020.
Section
Research Article

References

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