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Related Experiment Video

Updated: Jul 2, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

SpinachXAI-Rec: a multi-stage explainable AI framework for spinach freshness classification and consumer

Akella S Narasimha Raju1, G Sujatha2, Ranjit Kumar Gatla3

  • 1Department of Computing Technologies, School of Computing, College of Engineering & Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, 603203, India. akellar@srmist.edu.in.

Scientific Reports
|October 14, 2025
PubMed
Summary

This study introduces SpinachXAI-Rec, an AI framework using deep learning to automatically classify spinach freshness. It provides interpretable AI recommendations for safe consumption, enhancing food safety.

Keywords:
Deep feature embeddingsDenseNet121Explainable AI (XAI)GradCAM++LIMERule-based recommender systemSpinach freshness classificationVision transformer (ViT)

Related Experiment Videos

Last Updated: Jul 2, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Area of Science:

  • Computer Science
  • Food Science
  • Artificial Intelligence

Background:

  • Spinach is nutritious but highly perishable, leading to spoilage and health risks.
  • Traditional freshness assessment methods are subjective, time-consuming, and unreliable.
  • Defective consumption decisions impact human health and food safety.

Purpose of the Study:

  • To develop an automated, AI-driven framework (SpinachXAI-Rec) for classifying spinach freshness.
  • To provide interpretable AI and actionable consumer recommendations for spinach.
  • To enhance food safety through accurate freshness validation.

Main Methods:

  • A dataset of 12,000 spinach images across three varieties and six categories (fresh/non-fresh) was created.
  • Deep learning models (DenseNet121, ResNet50, EfficientNetB0) were trained and evaluated.
  • Explainable AI techniques (GradCAM++, LIME) and a rule-based recommender system were integrated.

Main Results:

  • DenseNet121 achieved 96% classification accuracy in Stage 1.
  • The combined DenseNet121 + ViT-B/16 + SVM model achieved an F1-score of 0.97 and high segmentation precision (Dice 0.89, IoU 0.82).
  • The framework successfully categorized spinach into 'Eatable', 'Eatable with Caution', or 'Not Eatable'.

Conclusions:

  • SpinachXAI-Rec offers an accurate and interpretable AI solution for spinach freshness assessment.
  • The system empowers consumers and industry stakeholders with informed, health-conscious decisions.
  • This advancement contributes to safer food systems through AI-driven validation and recommendations.