A Simple Multispectral Imaging Algorithm for Detection of Defects on Red Delicious Apples

Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, Agricultural Research Service, United States Department of Agriculture, 10300 Baltimore Ave, Beltsville, MD 20705 National Academy of Agricultural Science, Rural Development Administration, Suwon 441-100, Republic of Korea Department of Biosystems Machinery Engineering, Chungnam National University, Daejeon, 305-764, Republic of Korea School of Biosystems Engineering, University College Dublin, Dublin 4, Ireland Received: April 25th 2014; Revised: May 26th 2014; Accepted: June 1st 2014 Purpose: A multispectral algorithm for detection and differentiation of defective (defects on apple skin) and normal Red Delicious apples was developed from analysis of a series of hyperspectral line-scan images. Methods: A fast line-scan hyperspectral imaging system mounted on a conventional apple sorting machine was used to capture hyperspectral images of apples moving approximately 4 apples per second on a conveyor belt. The detection algorithm included an apple segmentation method and a threshold function, and was developed using three wavebands at 676 nm, 714 nm and 779 nm. The algorithm was executed on line-by-line image analysis, simulating online real-time line-scan imaging inspection during fruit processing. Results: The rapid multispectral algorithm detected over 95% of defective apples and 91% of normal apples investigated. Conclusions: The multispectral defect detection algorithm can potentially be used in commercial apple processing lines.