Development of a Multi-machine Learning Model Fusion and Stacking Approach for Online Sorting of Zhaotong Sugar-Heart Apples Using Visible/Near-Infrared Spectroscopy

In view of the uncertainty of sugar heart occurrence in Zhaotong apples and the varying effects of different sugar heart degrees on fruit flavor and storage life, this study aimed to develop a rapid and non-destructive method for accurately sorting apples with or without different degrees of sugar heart, thereby providing technical support for enhancing the commercial value of fruits and optimizing their storage and grading. This study employed a laboratory-developed online fruit sorting system to acquire visible/near infrared (VIS/NIR) spectral signals of Zhaotong sugar-heart apples. Feature wavelengths identified by the variable importance in projection (VIP) method served as inputs for establishing a stacking ensemble model integrating random forest (RF), extreme gradient boosting (XGBoost), logistic regression (LR), and support vector machine (SVM) algorithms. The performance of the stacking model was compared against that of standalone models. The results showed that the stacking model integrating four distinct machine learning algorithms exhibited superior recognition performance with an accuracy of 95.47%, a true positive rate (TPR) of 94.82%, and a true negative rate (TNR) of 97.83% compared with standalone models. The proposed stacking ensemble approach significantly enhances predictive capability by combining the strengths of base models without substantially increasing the computational load, showing great potential for applications in online fruit sorting.