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Image-processing algorithms for tomato classification
Year:
2002
Authors :
Alchanatis, Victor
;
.
Fallik, Elazar
;
.
Volume :
45
Co-Authors:
Laykin, S., Dept. of Indust. Eng. and Management, Ben Gurion University of the Negev, Beer Sheva, Israel
Alchanatis, V., Agricultural Research Organization, Inst. of Agricultural Engineering, Bet Dagan, Israel
Fallik, E., Agricultural Research Organization, Dept. of Postharvest Sci. of F. P., Bet Dagan, Israel
Edan, Y., Dept. of Indust. Eng. and Management, Ben Gurion University of the Negev, Beer Sheva, Israel, Dept. of Indust. Eng. and Management, Ben Gurion University of the Negev, P.O. Box 653, Beer Sheva 84105, Israel
Facilitators :
From page:
851
To page:
858
(
Total pages:
8
)
Abstract:
Image-processing algorithms were developed and implemented to provide the following quality parameters for tomato classification: color, color homogeneity, defects, shape, and stem detection. The vision system consisted of two parts: a bottom vision cell with one camera facing upwards, and an upper vision cell with two cameras viewing the fruit at 60°. The bottom vision cell determined fruit stem and shape. The upper vision cell determined fruit color, defects, and color homogeneity. Experiments resulted in 90% correct bruise classification with 2% severely misclassified; 90% correct color homogeneity classification; 92% correct color detection with 2% severely misclassified, and 100% stem detection.
Note:
Related Files :
agricultural application
Agriculture
color
Fruits
image analysis
Machine vision
Tomato classification
Tomatoes
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More details
DOI :
Article number:
Affiliations:
Database:
Scopus
Publication Type:
article
;
.
Language:
English
Editors' remarks:
ID:
24796
Last updated date:
02/03/2022 17:27
Creation date:
17/04/2018 00:10
Scientific Publication
Image-processing algorithms for tomato classification
45
Laykin, S., Dept. of Indust. Eng. and Management, Ben Gurion University of the Negev, Beer Sheva, Israel
Alchanatis, V., Agricultural Research Organization, Inst. of Agricultural Engineering, Bet Dagan, Israel
Fallik, E., Agricultural Research Organization, Dept. of Postharvest Sci. of F. P., Bet Dagan, Israel
Edan, Y., Dept. of Indust. Eng. and Management, Ben Gurion University of the Negev, Beer Sheva, Israel, Dept. of Indust. Eng. and Management, Ben Gurion University of the Negev, P.O. Box 653, Beer Sheva 84105, Israel
Image-processing algorithms for tomato classification
Image-processing algorithms were developed and implemented to provide the following quality parameters for tomato classification: color, color homogeneity, defects, shape, and stem detection. The vision system consisted of two parts: a bottom vision cell with one camera facing upwards, and an upper vision cell with two cameras viewing the fruit at 60°. The bottom vision cell determined fruit stem and shape. The upper vision cell determined fruit color, defects, and color homogeneity. Experiments resulted in 90% correct bruise classification with 2% severely misclassified; 90% correct color homogeneity classification; 92% correct color detection with 2% severely misclassified, and 100% stem detection.
Scientific Publication
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