Abstract
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.
Research project is partially supported by the program “Excellence Initiative - research university” for the AGH University of Krakow and is partially supported by a Grant for Statutory Activity from the Faculty of Physics and Applied Computer Science of the AGH.
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Notes
- 1.
Data set available on the website https://archive.ics.uci.edu/dataset/236/seeds.
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Kowalski, P.A., Jeczmionek, E., Charytanowicz, M., Łukasik, S., Niewczas, J., Kulczycki, P. (2025). Classification of Wheat Species Using Convolutional Neural Networks: A Comparative Study. In: Perakovic, D., Knapcikova, L. (eds) Future Access Enablers for Ubiquitous and Intelligent Infrastructures. FABULOUS 2024. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 596. Springer, Cham. https://doi.org/10.1007/978-3-031-72393-3_1
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