Abstract:

[Significance] Apple industry is a prominent agricultural sector that is of considerable importance global. 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. [Progress] 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 like 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, enhancing 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, presenting 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. [Conclusions and Prospects] 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 must 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 postharvest 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.

Key words: multi-source data fusion, apple, non-destructive detection, quality evaluation, disease diagnosis, spectrum, electronic nose, machine vision