Apple industry is a prominent agricultural sector that is of considerable importance globally. Ensuring the highest standards of quality and safety is paramount for achieving consumer satisfaction. Non-destructive testing technologies have emerged as a powerful tool, enabling rapid and objective evaluation of fruit attributes. However, individual non-destructive testing technologies methods frequently possess inherent limitations, proving insufficient for comprehensive assessment. The synergistic application of multi-source data fusion technology in the non-destructive testing integrates information from multiple sensors to overcome the shortcomings of single-modality systems. The integration of disparate data streams constitutes the foundational technological framework that enables the advancement of apple quality control. This technological framework facilitates enhanced detection of defects and diseases, thereby contributing to the intelligent transformation of the apple industry value chain in its entirety.

This paper presents a systematic and comprehensive examination of recent advancements in multi-source data fusion for apple non-destructive testing. The principles, advantages, and typical application scenarios of five mainstream non-destructive testing technologies are first introduced: near-infrared (NIR) spectroscopy, particularly adept at quantifying internal chemical compositions such as soluble solids content (SSC) and firmness by analyzing molecular vibrations; hyperspectral imaging (HSI), which combines spectroscopy and imaging to provide both spatial and spectral information, making it ideal for visualizing the distribution of chemical components and identifying defects like bruises; electronic nose (E-nose) technology, a method for detecting unique patterns of volatile organic compounds (VOCs) to profile aroma and detect mold; machine vision, a process that analyzes external features such as color, size, shape, and texture for grading and surface defect identification; and nuclear magnetic resonance (NMR), a technique that provides detailed insights into internal structures and water content, useful for detecting internal defects such as core rot. A critical evaluation of the fundamental methodologies in data fusion is conducted, with these methodologies categorized into three distinct levels. Data-level fusion entails the direct concatenation of raw data from homogeneous sensors or preprocessed heterogeneous sensors. This approach is straightforward. It can result in high dimensionality and is susceptible to issues related to data co-registration. Feature-level fusion, the most prevalent strategy, involves extracting salient features from each data source (e.g., spectral wavelengths, textural features, gas sensor responses) and subsequently combining these feature vectors prior to model training. This intermediate approach effectively reduces redundancy and noise, and enhances model robustness. Decision-level fusion operates at the highest level of abstraction, where independent models are trained for each data modality, and their outputs or predictions are integrated using algorithms such as weighted averaging, voting schemes, or fuzzy logic. This strategy offers maximum flexibility for integrating highly disparate data types. The paper also thoroughly elaborates on the practical implementation of these strategies, and presents case studies on the fusion of different spectral data (e.g., NIR and HSI), the integration of spectral and E-nose data for combined internal quality and aroma assessment, and the powerful combination of machine vision with spectral data for simultaneous evaluation of external appearance and internal composition.

The integration of multi-source data fusion technology has driven significant advancements in the field of apple non-destructive testing. This progress has substantially improved the accuracy, reliability, and comprehensiveness of quality evaluation and control systems. By synergistically combining the strengths of different sensors, it enables a holistic assessment that is unattainable with any single technology. However, the field faces persistent challenges, including the effective management of data heterogeneity (i.e., varying scales, dimensions, and physical meanings), the high computational complexity of sophisticated fusion models, and the poor portability of current multi-sensor laboratory equipment—all of which hinder online industrial applications. Future research should prioritize several key areas. First, developing automated, user-friendly fusion platforms is imperative to simplify data processing and model deployment. Second, optimizing and developing lightweight algorithms (e.g., through model compression and knowledge distillation) is critical to enhancing real-time performance for high-throughput sorting lines. Third, creating compact, cost-effective, integrated hardware that combines multiple detection technologies into a single portable device will improve stability and accessibility. Additionally, new application frontiers should be explored, such as in-field monitoring of fruit maturation and predicting post-harvest shelf life. The innovative integration of advanced algorithms and hardware holds the potential to provide substantial support for the intelligent and sustainable development of the global apple industry.

Moldy core disease is a common internal disease of the apple and is highly infectious. In the early storage stage, the mold symptoms are confined to the interior of the core. The apple tissue is in a sub-healthy state and still has commercial value, so early detection of moldy-core apples is critical.

In this study, a non-destructive acoustic vibration detection system was used to acquire acoustic vibration response signals. Symmetrized dot pattern (SDP), gramian angular field (GAF), and stockwell transform (ST) were applied to obtain multi-domain acoustic vibration spectra, including SDP images, gramian angular summation field (GASF) images, gramian angular difference field (GADF) images, and ST images. These images were uniformly converted to grayscale, transforming the time-domain signals into multi-domain visual spectra to facilitate subsequent feature analysis and recognition. Uniform local binary pattern (ULBP) and gray-level-gradient co-occurrence matrix (GLGCM) methods were used to extract handcrafted features from the multi-domain visualized images. Subsequently, the maximum relevance and minimum redundancy (mRMR) criterion was applied to select the dominant features from each analysis domain that were sensitive to early disease information. Principal component analysis (PCA) was employed to reduce the dimensionality of the multi-domain spectral ULBP texture features. From the statistical features extracted from one-dimensional acoustic-vibration signals in the time and frequency domains, and the GLGCM texture features extracted from two-dimensional images in each domain, 5 to 8 features sensitive to early moldy core detection were selected. From the high-dimensional sensitive ULBP texture features extracted from each domain, 3 to 7 principal components were obtained through dimensionality reduction using principal component analysis. This selection aimed to maximize the relevance between features and class labels while minimizing redundancy among features, thereby identifying the most informative features for early mold core apple detection in each domain. Meanwhile, a ResNet50 feature extractor improved with a convolutional attention mechanism module and the Adam optimizer (Adam-IResNet50) was designed to automatically extract deep features from visualized images in each domain. The optimal shallow features were used to train a multiple support vector machine (MSVM) classifier, while the optimal deep features were used to train an extreme learning machine (ELM) classifier. The Adam-IResNet50 network was employed as a feature extractor. The deep features extracted from the time-domain and frequency-domain GADF images, as well as time-frequency images, resulted in higher sample matching scores (SC) and cluster compactness (CHS) values, along with lower inter-class overlap (DBI) values for the three apple categories. These results clearly indicate that the deep features extracted by the Adam-IResNet50 model from multi-domain images exhibit strong capability in identifying subhealth and moldy core apples. The preliminary outputs of the two classifiers were converted into basic probability assignments for independent evidence bodies. Dempster's combination rule and the associated decision criterion of Dempster-Shafer (D-S) theory were then applied to yield the final decision on early-stage moldy apples. Consequently, a decision-level fusion model was established for both shallow and deep features of the acoustic-vibration multi-domain spectra.

The constructed Adam-IResNet50-IPSO-ELM-DS model based on D-S evidence theory achieved a Kappa coefficient and Matthews Correlation Coefficient (MCC) slightly below 90% for multi-class classification of apples from known origins. The F₁-Score and Overall Accuracy (OA) reached 93.01% and 93.22%, respectively. The classification accuracy for sub-healthy apples was 87.37%, while the misclassification rate for diseased apples was 8.33%. These results indicate that the model maintains a balanced precision and recall while achieving high detection accuracy for three classes of apples from unknown origins. After decision fusion, the IPSO-MSVM-DS and Adam-IResNet50-IPSO-ELM-DS models demonstrated significant performance improvements. Among them, the Adam-IResNet50-IPSO-ELM-DS model achieved an accuracy of 93.22%, which was significantly higher than that of other methods. This demonstrates that decision-level fusion could effectively enhance the model's discriminative ability and further improve classification performance.

The proposed acoustic vibration detection method for mold core apples, based on Dempster-Shafer evidence theory, provides technical support for future online batch detection of early mold core apples. Early screening of sub-healthy apples is of great significance for quality control during postharvest storage. In future work, the model will be further optimized to develop a rapid acoustic vibration-based prediction method for early detection of mold core, providing technical support for quality control during apple distribution.