Building a rule-based machine-vision system for defect inspection on apple sorting and packing lines

A near-infrared machine-vision system was developed for automating apple defect inspection. Fast blob extraction from fruit images was performed by using an adaptive spherical transformation A binary decision-tree-structured rule base was established using blob feature extraction and analysis. Both off-line and on-line test results demonstrated that the rule-based system was effective for apple defect detection. Compared with the neural network method, the rules-based approach had more flexibility for changing or adding parameters, features and rules to meet various sorting requirements. The technique presented in this paper is being commercialized by a leading manufacturer of fruit/vegetable packinghouse equipment. (C) 1999 Elsevier Science Ltd. All rights reserved.