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Updated: Oct 15, 2025

Deep Neural Networks for Image-Based Dietary Assessment
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ACHENY: A standard Chenopodiaceae image dataset for deep learning models.

Ahmad Heidary-Sharifabad1, Mohsen Sardari Zarchi2, Sima Emadi3

  • 1Department of Computer Engineering, Maybod Branch, Islamic Azad University, Maybod, Iran.

Data in Brief
|October 29, 2021
PubMed
Summary

This study introduces the ACHENY dataset, featuring 27,030 images of 30 wild Chenopodiaceae species. This resource aids in developing efficient deep learning models for crucial plant biodiversity conservation efforts.

Keywords:
Biodiversity protectionChenopodiaceaeDeep learningImage classificationPlant classification

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Area of Science:

  • Botany and Plant Science
  • Computer Science and Artificial Intelligence
  • Biodiversity and Conservation

Background:

  • The Chenopodiaceae family, comprising approximately 1500 species, holds significant ecological importance globally.
  • Biodiversity conservation of these species is threatened by human activities, necessitating effective identification and surveillance methods.
  • Deep learning offers a promising approach for automated identification, but requires relevant, high-quality datasets.

Purpose of the Study:

  • To introduce the ACHENY dataset, a novel collection of images for training deep learning models.
  • To facilitate the development of efficient deep learning models for categorizing Chenopodiaceae species in their natural habitats.
  • To support biodiversity conservation efforts through improved plant identification technologies.

Main Methods:

  • The ACHENY dataset was collected from natural desert and semi-desert habitats in Iran.
  • It comprises 27,030 RGB images of 30 Chenopodiaceae species, with varying numbers of images per species (300-1461).
  • Images were captured under diverse real-world conditions (varying distances, viewpoints, angles, sunlight) and resized to 224x224 and 64x64 dimensions. Data was split into 72% training, 18% validation, and 10% testing sets.

Main Results:

  • The dataset is imbalanced, reflecting natural species distribution.
  • Images were collected without pre-processing, except for resizing, maintaining real-world variability.
  • The dataset is structured for direct use with various deep learning architectures.

Conclusions:

  • The ACHENY dataset provides a valuable resource for advancing deep learning applications in plant identification.
  • It enables the creation of efficient models for Chenopodiaceae species categorization, crucial for conservation.
  • This dataset supports research into automated biodiversity monitoring in challenging environments.