SugarWave: A Non-destructive Estimation of Fruit Sugar Content Using Millimeter-Wave Sensing

Fruit’s Soluble Sugar Content (SSC) indicates quality and ripeness, which is essential for fruit production. Quality control in fruit production optimizes shipping, storage, waste prevention, and consumer satisfaction. We investigate the feasibility of estimating SSC using a low-cost, non-destructive system based on 77-81 GHz mmWave sensing technology. SugarWave collects fruit reflection signals, trains machine learning models, and estimates SSC. Our study shows that SugarWave can correctly identify fruit quality with an accuracy as high as 83%. It can also measure the SSC, with a median error of 1.4 °Bx.