Study on Artificial Intellectual Methods Used in Automated Detection for Fruit Surface Defect

The images of apples with different kinds of defects were acquired and preprocessed with the computer vision technology.Using the SelfAdaptive Feature Clustering (SAFC) neural network and Fuzzy Weighted Decision Tree (FWDT) methods,the accurate detection and detailed classification of defective areas on apples were successively achieved.Experimental result showed that,in the detection of surface defects,artificial intellectual method has good antinoise and faulttolerance ability.It can effectively overcome the shortcomings of low adaptability of traditional image segmentation method,and thus improve the accuracy of defect detection and classification.