Browse > Article
http://dx.doi.org/10.30693/SMJ.2018.7.4.37

Design and Implementation of an Automated Fruit Quality Classification System  

Choi, Han Suk (department of Computer Engineering, Mokpo National University)
Publication Information
Smart Media Journal / v.7, no.4, 2018 , pp. 37-43 More about this Journal
Abstract
Most of fruit quality classification has been done by time consuming, inaccurate and intensive manual labor. This study proposed an automated fruit grading system based on appearances and internal flavors. In this study, image processing technique and a weight checker were used to measure the value of appearance features and the near infrared spectroscopy analysis method was used to estimate the value of internal flavors. Additionally, I suggested 8x8x5x5 ANN based fruit quality classifier model to grade fruits quality. The proposed automated fruit quality classification system is expected to be very beneficial for many farms where heavy manual labor is usually needed for fruit quality classification.
Keywords
automated fruit quality classification system; fruit appearance feature extraction; NIR spectroscopy analysis; fruit quality classifier;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Han S. Choi, Hong S. Choi, and H. D. Mun, " A Smart Fruits Quality Classification Hardware Design using the Near-Infraed Spectroscopy and Image Processing Technologies," International Conference on Convergence Content( ICCC 2016), pp. 9-10, 2016.
2 S. Naik and B. Patel, "Machine Vision based Fruit Clssification and Grading - A Review," International Journal of Computer Applications(0975-8887), vol. 170, No.9, pp. 22- 34, July, 2017.   DOI
3 Jong H. Shin, Digital Image processing, Hanbit Academy, 2015
4 Sang Cheol Park, Kang Han Oh, In Seop Na, Soo Hyung Kim, Hyung Jeong Yang, Guee Sang Lee, "Noise Removal for Level Set based Flower Segmentation," Smart Media Journal, vol. 1, no. 2, pp.34-39, June, 2012.
5 Mert R. Sabuncu, Entropy-based Image Regestration, Ph.D. Dissertation, Prinston University, Nov., 2006.
6 J. Yoon, M. So, C. Han, H. Y. Kim, "Comparison of Prediction Models of the Sugar Content for a Nondestructuve Sorting System Based on Near-infrared," Proceedings of KIIT Summer Conference, pp. 414-417, 2014.
7 Jacob Cohen, P. Cohen, S.G. West, L. S. Aiken, "Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences ," 3rd Edition, lawrence Erlbaum Associates, Publishers, 2003
8 I. Sa, Z. Ge, F. Dayoub, B. Upcroft, T. Perez, and C. McCool, "DeepFruit: A Fruit Detrection System using Deep Neural Networks," sensors 2016, 16, 1222, doi:10.3390/s116081222.
9 Hae-Min Moon, Jin-Won Park, Sung Bum Pan, "Performance Analysis of Face Recognition by Face Image resolutions using CNN without Backpropergation and LDA," Smart Media Journal, vol. 5, no. 1, pp.24-29, March, 2016.
10 Kaifeng Ly, Shunhua, Jian Li, "Learning Gradient Descent:Better Generalization and Longer Horizons," Proceedings of the 34th International Conference on machine Learning, Sydney Australia, PMIR 70 2017.
11 Sun Park, Jongwon Kim, "Red Tide Algea Image Classification using Deep Learning based Open Source," Smart Media Journal, vol. 7, no. 2, pp34-39, June, 2018.   DOI
12 Han Suk Choi, Je Bong Cho, Sang Gyun Kim, Hong Seok Choi, "A Real-Time Smart Fruit Quality Grading System Classifying by External Appearance and Internal Flavor Factors," IEEE 2018 International Conference on Industrial Technology(ICIT), pp. 321-326. Feb., 2018.