Ultrafast on-site adulteration detection and quantification in Asian black truffle using smartphone-based computer vision.

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.