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This paper introduces a novel image dataset tailored for evaluating machine learning solutions, particularly focusing on deep neural networks. Derived from X-ray images of wheat grains, the dataset encompasses three distinct species: Kama, Rosa, and Canadian. We provide a comprehensive overview of the dataset’s structure and conduct experiments using ten pretrained deep neural networks to classify wheat species. The Seeds Image Data Set offers a competitive alternative to established object recognition benchmarks such as CIFAR-10, CIFAR-100, SVHN, and ImageNet. Its compact size streamlines computational processes, making it an efficient resource for exploratory data analysis. The dataset will be publicly available, serving as a foundational resource for future research endeavors in the field.

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Classification of Wheat Species Using Convolutional Neural Networks: A Comparative Study

  • 会议论文

  • 发表日期：

    2025年

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  • Piotr A. Kowalski,
  • Ernest Jeczmionek,
  • Malgorzata Charytanowicz,
  • Szymon Łukasik,
  • Jerzy Niewczas,
  • Piotr Kulczycki

摘要

This paper introduces a novel image dataset tailored for evaluating machine learning solutions, particularly focusing on deep neural networks. Derived from X-ray images of wheat grains, the dataset encompasses three distinct species: Kama, Rosa, and Canadian. We provide a comprehensive overview of the dataset’s structure and conduct experiments using ten pretrained deep neural networks to classify wheat species. The Seeds Image Data Set offers a competitive alternative to established object recognition benchmarks such as CIFAR-10, CIFAR-100, SVHN, and ImageNet. Its compact size streamlines computational processes, making it an efficient resource for exploratory data analysis. The dataset will be publicly available, serving as a foundational resource for future research endeavors in the field.