Grading the damage degree of fresh Lycium barbarum L. fruits based on electrical characteristics

Fresh Lycium barbarum L. (L. barbarum) fruits are renowned for their exceptionally high nutritional value and health benefits, which is leading to an increasing demand among consumers. However, the quality testing and grading of fresh L. barbarum fruits present significant challenges that hinder the growth of the L. barbarum industry. In this study, an electrical characterization method is used to analyze the variations in electrical parameters of fresh L. barbarum fruits under different degrees of damage. Optimal testing conditions for eight electrical parameters are determined, and principal component analysis (PCA) along with partial least squares (PLS) is applied to reduce data dimensionality and extract key features. Subsequently, damage degree discrimination models are developed using the support vector machine (SVM), random forest (RF), and convolutional neural network (CNN). The experimental results indicate that the PLS-RF model was the most effective, achieving discrimination accuracies of 99.48% and 91.25% in the training and test sets, respectively. The aim of this study is to validate the feasibility of using electrical characteristics to differentiate the degree of fruit damage and it establishes a reliable model for assessing damage extent in L. barbarum fruits. This innovative approach not only provides a novel method for evaluating fruit damage but may also serve as a theoretical basis for the development of mechanical harvesting equipment for L. barbarum fruits.