TY - GEN
T1 - Computer vision grading system for physical quality evaluation of green coffee beans
AU - Portugal-Zambrano, Christian E.
AU - Gutierrez-Caceres, Juan C.
AU - Ramirez-Ticona, Juan
AU - Beltran-Castanon, Cesar A.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/1/25
Y1 - 2017/1/25
N2 - Evaluating the physical defects of green coffee beans are an important process in defining their quality. This evaluation is normally carried out by visual inspection or using traditional instruments which have some limitations. This work is focused on the implementation of a computer vision system combining a hardware prototype and a software module. The hardware was developed to capture the images of coffee beans, the software uses a White-Patch algorithm as a image enhancement procedure, color histograms as feature extractor and SVM for the classification task, a database of 1930 images was collected, we used 13 categories of defects described in the SCAA standard of evaluation. Results of classification achieved a 98.8% of overall detection accuracy, therefore the proposed system proved to be effective in classifying physical defects of green coffee beans. Finally a set of conclusions and future works are presented.
AB - Evaluating the physical defects of green coffee beans are an important process in defining their quality. This evaluation is normally carried out by visual inspection or using traditional instruments which have some limitations. This work is focused on the implementation of a computer vision system combining a hardware prototype and a software module. The hardware was developed to capture the images of coffee beans, the software uses a White-Patch algorithm as a image enhancement procedure, color histograms as feature extractor and SVM for the classification task, a database of 1930 images was collected, we used 13 categories of defects described in the SCAA standard of evaluation. Results of classification achieved a 98.8% of overall detection accuracy, therefore the proposed system proved to be effective in classifying physical defects of green coffee beans. Finally a set of conclusions and future works are presented.
KW - Grade System
KW - Histogram
KW - Image Enhancement
KW - Retinex
KW - SVM
KW - White Patch
KW - coffee beans defects
UR - http://www.scopus.com/inward/record.url?scp=85013941924&partnerID=8YFLogxK
U2 - 10.1109/CLEI.2016.7833383
DO - 10.1109/CLEI.2016.7833383
M3 - Conference contribution
AN - SCOPUS:85013941924
T3 - Proceedings of the 2016 42nd Latin American Computing Conference, CLEI 2016
BT - Proceedings of the 2016 42nd Latin American Computing Conference, CLEI 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 42nd Latin American Computing Conference, CLEI 2016
Y2 - 10 October 2016 through 14 October 2016
ER -