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Smartphone-based multi-criteria vegetable object detection dataset from Bangladesh.

Sabrina Jahan1, B M Shahria Alam1, Ishraque Manzur1

  • 1Department of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.

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This study introduces a new dataset for vegetable detection in Bangladesh, featuring 3534 images of 22 classes captured in real-world conditions. This resource supports computer vision models for smarter agricultural practices and improved food security.

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Agricultural PracticesComputer VisionImaging datasetVegetable detectionVegetable identification

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Area of Science:

  • Computer Vision
  • Agricultural Science
  • Data Science

Background:

  • Vegetable cultivation is vital for Bangladesh's nutrition, economy, and food security.
  • Accurate vegetable identification is crucial for efficient farming and smart agricultural practices.

Purpose of the Study:

  • To introduce a comprehensive, real-world dataset for vegetable detection.
  • To support the development of computer vision models for accurate vegetable recognition.
  • To aid decision-making in vegetable cultivation for sustainable agriculture.

Main Methods:

  • Collected 3534 high-resolution images of 22 distinct vegetable classes in natural settings.
  • Captured images using a Redmi Note 12 from roadside vendors, emphasizing ground-level perspectives.
  • Annotated images using Roboflow platform and provided the dataset in Pascal VOC format.

Main Results:

  • Developed a diverse dataset with 3534 images across 22 vegetable classes.
  • Dataset features natural variations in appearance, shape, and lighting conditions.
  • Dataset is suitable for training robust object detection models for handheld and low-cost systems.

Conclusions:

  • The new dataset enhances computer vision model development for vegetable recognition.
  • Facilitates smarter agricultural practices and contributes to national food security.
  • Supports sustainable agriculture through improved data-driven decision-making.