Detecting Starch-Head and Mildewed Fruit in Dried Hami Jujubes Using Visible/Near-Infrared Spectroscopy Combined with MRSA-SVM and Oversampling

Abstract

Dried Hami jujube has great commercial and nutritional value. Starch-head and mildewed fruit are defective jujubes that pose a threat to consumer health. A novel method for detecting starch-head and mildewed fruit in dried Hami jujubes with visible/near-infrared spectroscopy was proposed. For this, the diffuse reflectance spectra in the range of 400–1100 nm of dried Hami jujubes were obtained. Borderline synthetic minority oversampling technology (BL-SMOTE) was applied to solve the problem of imbalanced sample distribution, and its effectiveness was demonstrated compared to other methods. Then, the feature variables selected by competitive adaptive reweighted sampling (CARS) were used as the input to establish the support vector machine (SVM) classification model. The parameters of SVM were optimized by the modified reptile search algorithm (MRSA). In MRSA, Tent chaotic mapping and the Gaussian random walk strategy were used to improve the optimization ability of the original reptile search algorithm (RSA). The final results showed that the MRSA-SVM method combined with BL-SMOTE had the best classification performance, and the detection accuracy reached 97.22%. In addition, the recall, precision, F1 and kappa coefficient outperform other models. Furthermore, this study provided a valuable reference for the detection of defective fruit in other fruits.

JujubeNet: A high-precision lightweight jujube surface defect classification network with an attention mechanism

“…The introduction of classical techniques did solve the automated sorting of red jujube to a certain extent. However, they require a high inspection environment and have problems such as low accuracy and poor real-time performance, and, therefore, are not conducive to large-scale promotion ( Li et al., 2022 ). DL, a significant branch of machine learning, has made breakthroughs in recent years, especially with Convolutional Neural Networks (CNN), which have become widely used in various image recognition scenarios because of their powerful feature extraction and nonlinear representation capabilities, and some defect detection methods based on DL begin to have wide application in various industrial scenes.…”