Purpose <p>The non-destructive quality assessment of fruit and vegetables has become increasingly crucial due to consumer demand for high-quality produce, thus the need to ensure their quality and marketability. Spectral analysis offers a promising approach for assessing internal and external quality attributes. Recent advancements in deep learning (DL) combined with spectral analysis have shown significant potential in providing accurate and non-invasive quality evaluation. This review aims to provide a comprehensive overview of the integration of DL with spectral analysis for the non-destructive quality assessment of fruit and vegetables.</p> Methods <p>It focuses on various DL spectral analysis architectures and models, such as one-dimensional convolutional neural networks (1D-CNN), autoencoders (AE), and long short-term memory (LSTM) networks, used in modeling qualitative and quantitative attributes. The review also explores recent advancements such as model generalization, interpretability, spectral data augmentation, and automated hyperparameter optimization. Furthermore, the applications, future trends, and challenges in DL spectral analysis for evaluating the quality of fruit and vegetables are discussed.</p> Results <p>The integration of DL with spectral analysis has significantly advanced the non-destructive quality assessment of fruit and vegetables. Key quality attributes such as soluble solids content (SSC), dry matter content (DMC), and moisture content (MC) can be predicted with high accuracy, enhancing the overall quality control processes. DL spectral analysis models have demonstrated better performance in terms of robustness and accuracy compared to conventional methods.</p> Conclusion <p>Overall, DL spectral analysis has the potential to transform the non-destructive assessment of fruit and vegetable quality, offering substantial benefits for consumers, researchers, and industry practitioners.</p>