An Algorithm Based on MAE for Impurities Recognition of Puer Tea

Tea impurities can have adverse effects on both the quality and flavor of the beverage. Consequently, the recognition of impurities is of paramount importance during the tea production process, as it is crucial for the elimination of such contaminants. In response to the inefficiency of manual sorting of tea impurities, this paper proposes a method for recognizing impurities in Puer tea base on masked autoencoders (MAE). Firstly, various images of Puer tea with impurities were collected. The MAE method was then utilized to address the issue of pixel loss in the images, ensuring that such loss would not affect the efficiency of impurities recognition in Puer tea; Secondly, finetune and utilize the attention mechanism of the MAE method to improve the training efficiency of the pre-trained model, thereby obtaining a high-precision model for recognizing impurities in Puer tea; Finally, the model was validated, and the accuracy of recognizing impurities reached 97.66 %, greatly improving the accuracy rate of recognizing Puer tea impurities.