Computer vision and image-processing techniques have been found increasingly useful for the fruit automatic quality inspection and defect sorting operation. However

real-time fruit surface defect inspection and recognition is still a challenging project due to its complexity. In this paper

a fast approach for box-dimension estimation based on a dual-pyramid data structure is developed. Utilizing traditional fractal dimension and 4 oriented fractal dimensions as input values

a BP neural network is designed for identifying fruit defect area and stem

calyx concave area. The results of experiment show that the approach is effective for real-time defect identification and is accurate. The rate of correct classification is 93% and the executing time of microcomputer for recognition of one undefined blob on the surface of apple is 4~7ms.