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Related Experiment Video

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Deep Neural Networks for Image-Based Dietary Assessment
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Dry fruit image dataset for machine learning applications.

Vishal Meshram1, Chetan Choudhary1, Atharva Kale1

  • 1Vishwakarma Institute of Information Technology, Pune, India.

Data in Brief
|July 13, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a comprehensive dataset of over 11,500 dry fruit images, aiding in the accurate classification and recognition of varieties like almonds, cashews, raisins, and dried figs for machine learning applications.

Keywords:
Computer visionDehydrated fruitsFruit ClassificationFruit detectionImage classificationMachine learning

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Data Science

Background:

  • Dry fruits offer significant health benefits, including improved nutrition and reduced disease risk, making them a valuable dietary component.
  • The global dry fruit market is substantial and projected to grow, highlighting the economic importance of these commodities.
  • Accurate quality assessment of dry fruits often relies on visual appearance, necessitating high-quality, well-labeled imagery.

Purpose of the Study:

  • To develop a comprehensive, high-quality dataset of dry fruit images for machine learning.
  • To facilitate the classification and recognition of various dry fruit types and subtypes.
  • To support research, education, and potential medicinal applications related to dry fruits.

Main Methods:

  • Collection and processing of over 11,500 high-quality images of dry fruits.
  • Categorization of images into 12 distinct classes, including four main types (Almonds, Cashew Nuts, Raisins, Dried Figs) and three subtypes each.
  • Ensuring images represent diverse conditions for robust model training.

Main Results:

  • A dataset comprising 11,500+ processed images across 12 distinct dry fruit classes.
  • Detailed representation of Almonds, Cashew Nuts, Raisins, and Dried Figs with their subtypes.
  • The dataset provides a foundation for developing accurate dry fruit classification models.

Conclusions:

  • The created dataset is a valuable resource for machine learning models aimed at classifying and recognizing dry fruits.
  • This resource can advance research, education, and applications in the field of dry fruit analysis.
  • The dataset supports the growing need for automated quality assessment and identification in the dry fruit industry.