Asian black truffle Tuber sinense (BT) is a premium edible fungus with medicinal value, but it is often prone to adulteration. This study aims to develop a fast, non-destructive, automatic, and intelligent method for identifying BT. A novel lightweight convolutional neural network model incorporates knowledge distillation (FastBTNet) to improve model efficiency on smartphones while maintaining higher performance. The well-trained model coupled with a fast object location technique was further employed for the absolute quantification of adulteration in BT. Results showed that FastBTNet achieved 99.0 % classification accuracy, 8.5 % root mean squared error in predicting adulteration levels, and 5.3 s for predicting 1024 samples. Additionally, Grad-CAM was used to investigate the models' recognition mechanism, and this strategy received a perfect score in the greenness assessment. These methods were deployed in a smartphone app, "Truffle Identifier," which enables ultrafast on-site identification of a batch of samples and assists in predicting adulteration levels.