Extraction and classification of apple defects under uneven illumination based on machine vision

The extraction and classification of different kinds of defects can achieve a more sophisticated classification of apples. In this study, a region of interest (ROI) extraction algorithm based on background separation, brightness correction, and the global threshold was designed for the extraction of apple decay and bruise under uneven light conditions. After brightness correction, we calculate the average gray value of the apple region and multiply the value by 0.8 as the threshold. In this way, the threshold of different apple images can be determined automatically without a manual setting. After extracting ROI, we realized the classification of the two kinds of defects by extracting texture features. Based on the selected texture features, the support vector machine discriminant models were established. The accuracy of the model based on angular second moment was 94.8% and the model based on entropy was 94.7%. The total time from ROI extraction to classification was approximately 1.2 s. The results showed that this extraction and classification process had application prospects in practical detection classification. Practical Applications Ungraded fruits were not easy to store, transport, or sell at a price based on quality. The classification was directly related to fruit packaging, transportation, storage and sales of the results, and benefits, so it was very important to carry out fruit classification. Machine vision technology can achieve objective and nondestructive rapid detection. Many studies had achieved extraction of defective parts. However, they directly classified apples with defects into one category without further classification of types of the defects. In many classification standards, such as GBT10651-2008, they need to classify apples more finely according to the defect types. This study achieved extraction and classification of different kinds of apple defects by machine vision technology and achieved a satisfactory accuracy and detection speed, which provided theoretical support for the realization of strict and fine classification of apple in industrial applications in the future.

Automated summary

This study achieved the extraction and classification of different apple defects using machine vision technology, achieving satisfactory accuracy and detection speed. This provides theoretical support for the realization of strict and fine classification of apples in industrial applications in the future.