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ARS Home » Northeast Area » Kearneysville, West Virginia » Appalachian Fruit Research Laboratory » Innovative Fruit Production, Improvement, and Protection » Research » Publications at this Location » Publication #169356

Title: IDENTIFYING DEFECTS IN IMAGES OF ROTATING APPLES

Author
item BENNEDSEN, BENT
item Peterson, Donald
item TABB, AMY

Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/15/2005
Publication Date: 8/15/2005
Citation: Bennedsen, B., Peterson, D.L., Tabb, A. 2005. Identifying defects in images of rotating apples. Computers and Electronics in Agriculture. Published by Elsevier B.V. doi: 10.1016/j.compag.2005.01.003.

Interpretive Summary: An automated apple defect detection/sorting system would reduce packing labor costs, improve labor productivity, and provide the consumer with consistent high quality fruit. A novel defect detecting method was developed that uses multiple images of the rotated apple to improve accuracy. Test showed that over 90% of defects were detected while minimizing good apples classified as bad. An accurate defect detection method is a necessary key component in the development of an automated sorting system for apple.

Technical Abstract: An experimental machine vision system was used to identify surface defects on apples, including bruises. Images were captured through two optical filters at 740 nm and 950 nm, respectively. In the ensuing grey scale images, defects appeared as dark areas, however so did shadows and parts of the stem/calyx area. This paper reports a novel approach to locate the defects and eliminate other dark areas. The method is based on rotating the apples in front of the camera while multiple images are acquired. Dark areas, which are found at the same position, relative to the apple during the rotation represent defects, while other dark areas, which change shape and/or position from one frame to the next, are not classified as defects. In a test using 54 Pink Lady apples with 56 defects, the system successfully detected 52 defects or 92% of the defects, while providing two false positive. In another test with Ginger Gold Apples, where the rotation technique was combined with images of the stem and calyx regions 90% of the defects were detected with no false positives.