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According to the application scenario of pearl automatic sorting, dividing superior pearls into inferior ones will lead to serious loss of profits, and dividing defective pearls into superior ones will cause product quality disputes and enterprise reputation damage; Artificial intelligence technology based on deep learning is still difficult to explain and has poor robustness, which makes it difficult to achieve higher precision sorting technology. In order to measure the practical needs of pearl sorting accuracy and the limitations of improving accuracy under the existing technical framework, a method of improving sorting reliability through human disagreement intervention approach is proposed. This approach introduces two independent AI systems to pre-process pears classification, whose disagreements then bring human intervention. In such a way the reliability of machine algorithms is improved at relatively less human cost. The performance indices including disagreement precision index and additional cost index are defined and based on public dataset the proposed approach improves nearly 4% of the classification accuracy at only 4.1% increase of the human cost, which proves the effectiveness of the proposed approach.