TY - JOUR
T1 - Edge Detection Features to Evaluate Hardness of Dates Using Monochrome Images
AU - Manickavasagan, A.
AU - Al-Shekaili, H. N.
AU - Thomas, G.
AU - Rahman, M. S.
AU - Guizani, N.
AU - Jayas, D. S.
N1 - Funding Information:
Acknowledgments We thank The Research Council of Sultanate of Oman for funding this study (Project No. RC/AGR/SWAE/11/01— Development of Computer Vision Technology for Quality Assessment of Dates in Oman). The preliminary results of this work were presented at the International Conference on Computing, Engineering and Communication Technologies (ICCECT), August 14–15, 2013 in Bangkok, Thailand.
PY - 2014/7
Y1 - 2014/7
N2 - Date is an important fruit in the regular diets of many peoples in the Arab countries and several other parts of the world. Hardness is one of the important attributes in determining the quality of dates. Hard dates are tough, difficult to chew, unsuitable for several product preparation and ultimately fetching low market price. In general, hard dates have strong curvy and zigzag textured skin. In this study, the efficiency of edge detection features in classifying dates based on hardness using monochrome images was determined. Date samples (Fard variety) were obtained from three major dates growing regions in Oman, and classified into three grades (soft, semi-hard and hard) by a group of trained graders followed with a confirmation by an experienced grader in a commercial dates company. Individual dates were imaged using a monochrome camera (600 dates per grade; total = 1,800 images). A total of 36 features were extracted (28 in spatial domain and 8 in frequency domain) using edge detection methods. An artificial neural network (ANN) was used to classify the dates based on hardness. The overall classification accuracies were 75 % and 87 % while using single ANN (irrespective of regions) for three-class (soft, semi-hard and hard) and two-class (soft and hard (semi-hard and hard together)) models, respectively. While using separate ANN for each region in the three-class model, the mean classification accuracies were 94 %, 59 % and 84 % for soft, semi-hard and hard dates, respectively. Similarly, for the two-class ANN model for each region, the accuracies were 95 % and 77 % for soft and hard dates, respectively. Edge detection features have a great potential in determining several surface qualities of food and agricultural products, where similar gray or color values but varying texture are found.
AB - Date is an important fruit in the regular diets of many peoples in the Arab countries and several other parts of the world. Hardness is one of the important attributes in determining the quality of dates. Hard dates are tough, difficult to chew, unsuitable for several product preparation and ultimately fetching low market price. In general, hard dates have strong curvy and zigzag textured skin. In this study, the efficiency of edge detection features in classifying dates based on hardness using monochrome images was determined. Date samples (Fard variety) were obtained from three major dates growing regions in Oman, and classified into three grades (soft, semi-hard and hard) by a group of trained graders followed with a confirmation by an experienced grader in a commercial dates company. Individual dates were imaged using a monochrome camera (600 dates per grade; total = 1,800 images). A total of 36 features were extracted (28 in spatial domain and 8 in frequency domain) using edge detection methods. An artificial neural network (ANN) was used to classify the dates based on hardness. The overall classification accuracies were 75 % and 87 % while using single ANN (irrespective of regions) for three-class (soft, semi-hard and hard) and two-class (soft and hard (semi-hard and hard together)) models, respectively. While using separate ANN for each region in the three-class model, the mean classification accuracies were 94 %, 59 % and 84 % for soft, semi-hard and hard dates, respectively. Similarly, for the two-class ANN model for each region, the accuracies were 95 % and 77 % for soft and hard dates, respectively. Edge detection features have a great potential in determining several surface qualities of food and agricultural products, where similar gray or color values but varying texture are found.
KW - Canny
KW - Dates
KW - Hardness
KW - Roberts
KW - Sobel
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U2 - 10.1007/s11947-013-1219-0
DO - 10.1007/s11947-013-1219-0
M3 - Article
AN - SCOPUS:84903716244
SN - 1935-5130
VL - 7
SP - 2251
EP - 2258
JO - Food and Bioprocess Technology
JF - Food and Bioprocess Technology
IS - 8
ER -