Capacitance based Non-destructive technique for post-harvest sugarcane quality prediction

Post-harvest deterioration of sugarcane quality is one of the major issues that affect sugar production in sugar industries. There is a need for an effective technique that could quickly measure sugarcane quality without pre-processing the sugarcane stalks. This study was intended to create a capacitance-based setup capable of predicting degrees brix through nondestructive testing. The setup measures relative permittivity values which along with weight is used to predict the value of degrees brix in sugarcane stalks. A 5V sinusoidal wave of 5 different frequencies in the range 1 kHz - 1 MHz was swept across the parallel plate capacitor with sugarcane stalk samples as the dielectric medium. A total of three hundred stalks from four commercial varieties of sugarcane (CO 95020, CO 86010, CO 86032 and CO 06022) were used for the analysis. A predictive model for degrees brix was built using multiple linear regression, random forest and artificial neural networks. Feature selection revealed that relative permittivity at 1kHz frequency and weight were highly capable of predicting degrees brix. The best prediction was obtained from the random forest model with a coefficient of determination (Rv2) of 0.942.