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Updated: Jul 1, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
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End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

A Validation-Driven Explainable Deep Ensemble Framework for Image-Based Saffron Adulteration Detection.

Syed Nisar Hussain Bukhari1, Kingsley A Ogudo2

  • 1National Institute of Electronics and Information Technology (NIELIT) J&K, Srinagar 191132, India.

Foods (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces a robust deep learning ensemble for detecting saffron adulteration using images. The validated framework ensures reliable authentication, outperforming individual models and offering explainable results for food quality control.

Area of Science:

  • Food Science
  • Computer Science
  • Analytical Chemistry

Background:

  • Saffron (Crocus sativus L.) is a high-value spice susceptible to adulteration.
  • Conventional authentication methods are limited for rapid, non-destructive analysis.
  • Existing deep learning approaches often lack rigorous validation and statistical reliability.

Purpose of the Study:

  • To develop a validation-driven and explainable deep ensemble framework for image-based saffron adulteration detection.
  • To ensure robust and statistically reliable authentication of saffron quality.
  • To provide methodological insights for food adulteration detection using limited data.

Main Methods:

  • Integration of pretrained convolutional neural networks (DenseNet169, ResNet50, VGG16) using a validation-driven weighted ensemble.
Keywords:
Crocus sativus L.convolutional neural networksdeep ensemble learningexplainable artificial intelligenceimage-based authenticationsaffron adulteration detectiontransfer learningvalidation-driven learning

Related Experiment Videos

Last Updated: Jul 1, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

  • Fusion weights computed from validation performance within training folds to prevent information leakage.
  • Stratified five-fold cross-validation and statistical tests (McNemar's, 5x2 cv) for performance validation.
  • Grad-CAM for explainability and background-invariance analysis for robustness.

    • Main Results:

    • Achieved 98.61% classification accuracy, 98.17% F1-score, and 98.61% AUC.
    • Outperformed the best individual base model by up to 1.4% in F1-score.
    • Demonstrated stable performance with mean accuracy of 97.81% ± 0.53 via cross-validation.
    • Statistical validation confirmed reliable performance improvements.

    Conclusions:

    • The proposed deep ensemble framework offers a robust, interpretable, and statistically validated solution for saffron authentication.
    • The method addresses limitations of conventional techniques and single deep learning models.
    • Provides a reliable approach for image-based food adulteration detection, especially under limited data conditions.