Cooking Ingredient Recognition Based on the Load on a Chopping Board during Cutting

This paper presents a method for recognizing recipe ingredients based on the load on a chopping board when ingredients are cut. The load is measured by four sensors attached to the board. Each chop is detected by indentifying a sharp falling edge in the load data. The load features, including the maximum value, duration, impulse, peak position, and kurtosis, are extracted and used for ingredient recognition. Experimental results showed a precision of 98.1% in chop detection and 67.4% in ingredient recognition with a support vector machine (SVM) classifier for 16 common ingredients.