Nondestructive Detection of Apple Mouldy Core Disease Based on Unbalanced Dielectric Data

In order to establish a nondestructive detection model for mouldy core disease in apples, 265 samples of healthy and suspected disease fruits were tested for 11 dielectric parameters in the 100hz - 3.98Mhz frequency. After dissection, the 17 diseased apples were observed. The Extra-Tree algorithm was used to extract the characteristics of samples. It found that the parameters of loss factor, conductance, negative impedance phase, parallel resistance, series resistance, and admittance in the low-frequency band of 100hz-1580hz had strong discriminability. As the number of healthy fruits and diseased fruits are unbalanced, using the SMOTE and other sampling techniques. Then, six classifiers were used to build the discriminant models. Experiments show that SMOTE-Tomek Link technique maintains the data distribution better, and GBDT classifier has the highest accuracy of detection, up to 95.97%. Thus, verifying the effectiveness of apple mouldy core disease nondestructive detection model based on unbalanced dielectric data.