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Multi-Task NoisyViT for Enhanced Fruit and Vegetable Freshness Detection and Type Classification.

Siavash Esfandiari Fard1, Tonmoy Ghosh1, Edward Sazonov1

  • 1Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35401, USA.

Sensors (Basel, Switzerland)
|October 16, 2025
PubMed
Summary
This summary is machine-generated.

A new multi-task Noisy Vision Transformer (NoisyViT) model accurately detects fruit and vegetable freshness using images. This AI approach offers a scalable solution for automated quality assessment in supply chains and retail.

Keywords:
computer visionfruit and vegetable freshness detectionfruit freshness detectionmulti-task learningpersonal healthcaresensor-based classificationvision transformer

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Area of Science:

  • Computer Vision and Artificial Intelligence
  • Agricultural Technology
  • Food Science and Technology

Background:

  • Fruit and vegetable freshness is crucial for quality, nutrition, and waste reduction, necessitating accurate assessment methods.
  • Traditional manual quality assessment is subjective and inefficient, driving the need for automated solutions.
  • Imaging sensors combined with Artificial Intelligence (AI) offer promising avenues for objective and scalable quality monitoring.

Purpose of the Study:

  • To evaluate the efficacy of the Noisy Vision Transformer (NoisyViT) model for automated fruit and vegetable freshness detection from images.
  • To develop and assess a multi-task NoisyViT model for simultaneous freshness and type classification, enhancing generalization.
  • To establish a robust and scalable AI solution for real-time quality assessment across the food supply chain.

Main Methods:

  • The NoisyViT model was initially tested on five public datasets, achieving high accuracies for freshness detection.
  • A unified dataset, Freshness44, was created by merging five datasets, comprising 44 classes across 22 fruit and vegetable types.
  • The NoisyViT architecture was adapted into a multi-task configuration with separate classification heads for freshness (binary) and type (22-class) identification, fine-tuned on Freshness44.

Main Results:

  • The single-head NoisyViT model demonstrated high accuracy (over 97%) on individual datasets.
  • The multi-task NoisyViT model achieved exceptional accuracies of 99.60% for freshness detection and 99.86% for type classification on the Freshness44 dataset.
  • The multi-task model outperformed the single-head NoisyViT and conventional machine learning/CNN-based methods in classification accuracy.

Conclusions:

  • The multi-task NoisyViT model, trained on the comprehensive Freshness44 dataset, provides a highly effective and accurate solution for automated fruit and vegetable freshness detection.
  • This AI-driven approach offers a scalable and robust system for real-time quality monitoring applicable in supply chains, retail, and consumer settings.
  • The study highlights the potential of advanced AI architectures like NoisyViT for addressing critical challenges in food quality assessment and waste reduction.