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A dataset revolutionizing Indian bay leaf analysis.

Priyanka Paygude1, Sandip Thite2, Ajay Kumar3

  • 1Bharati Vidyapeeth (Deemed to be University) College of Engineering, Pune, India.

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|November 18, 2024
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Summary
This summary is machine-generated.

A new dataset of Indian bay leaf images aids quality assessment. This resource supports machine learning for authenticating spices and improving the Indian spice industry.

Keywords:
ClassificationIndian bay leaf datasetIndian bay leaf quality assessmentMachine learning

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Food Science

Background:

  • Indian bay leaf is vital to cuisine, but quality and authenticity are frequently compromised.
  • Automated quality assessment is needed to ensure spice integrity.

Purpose of the Study:

  • To introduce a comprehensive, high-resolution image dataset of Indian bay leaf samples.
  • To facilitate research in leaf condition analysis and machine learning for quality assessment.

Main Methods:

  • Collected 5696 high-resolution images of Indian bay leaf samples.
  • Captured images under controlled conditions with variations in lighting, background, and leaf orientation.
  • Categorized samples into fresh, dried, and diseased-prone conditions.

Main Results:

  • Developed The Digital Indian Bay leaf dataset, a diverse and standardized resource.
  • The dataset covers various leaf conditions and imaging parameters.
  • Provides a foundation for developing machine learning models for bay leaf quality.

Conclusions:

  • The Digital Indian Bay leaf dataset is a valuable resource for researchers.
  • It will accelerate advancements in automated spice quality assessment.
  • Aims to enhance the overall Indian spice industry through improved authenticity and quality control.