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Near-Infrared (NIR) spectroscopy offers a powerful non-destructive approach for detecting internal defects in mangoes, such as spongy tissue. In this study, both lower wavelength (LW) and higher wavelength (HW) NIR spectral regions were analyzed. A customized preprocessing pipeline is implemented using techniques such as Savitzky–Golay smoothing, Multiplicative Scatter Correction (MSC), Extended MSC (EMSC), Standard Normal Variate (SNV), and detrending to enhance spectral quality. For dimensionality reduction, a tailored Autoencoder with Multi-Head Attention (AE-MHA) is employed. Extensive experiments were conducted using both AE-MHA and boosting-based classifiers (LightGBM and XGBoost), with all models evaluated using 5-fold cross-validation. The AE-MHA model, particularly on HW data, achieved the highest performance 93.75% overall accuracy and 100% accuracy in identifying defective mangoes. The HW spectral region significantly outperformed LW and also surpassed previous methods reported by Guru and Nandini (2025). This confirms the effectiveness of the proposed non-invasive framework for real-time detection of internal fruit defects, showing strong potential for integration into smart agriculture systems.

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Autoencoder-Based Deep Features for Internal Defect Detection in Mangoes Using NIR Spectral Data

  • 会议论文

  • 发表日期：

    2026年

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  • D. Nandini,
  • D. S. Guru

摘要

Near-Infrared (NIR) spectroscopy offers a powerful non-destructive approach for detecting internal defects in mangoes, such as spongy tissue. In this study, both lower wavelength (LW) and higher wavelength (HW) NIR spectral regions were analyzed. A customized preprocessing pipeline is implemented using techniques such as Savitzky–Golay smoothing, Multiplicative Scatter Correction (MSC), Extended MSC (EMSC), Standard Normal Variate (SNV), and detrending to enhance spectral quality. For dimensionality reduction, a tailored Autoencoder with Multi-Head Attention (AE-MHA) is employed. Extensive experiments were conducted using both AE-MHA and boosting-based classifiers (LightGBM and XGBoost), with all models evaluated using 5-fold cross-validation. The AE-MHA model, particularly on HW data, achieved the highest performance 93.75% overall accuracy and 100% accuracy in identifying defective mangoes. The HW spectral region significantly outperformed LW and also surpassed previous methods reported by Guru and Nandini (2025). This confirms the effectiveness of the proposed non-invasive framework for real-time detection of internal fruit defects, showing strong potential for integration into smart agriculture systems.