Szczegóły publikacji
Opis bibliograficzny
Classification of wheat species using convolutional neural networks: a comparative study / Piotr A. KOWALSKI, Ernest JĘCZMIONEK, Małgorzata Charytanowicz, Szymon ŁUKASIK, Jerzy Niewczas, Piotr KULCZYCKI // W: Future access enablers for ubiquitous and intelligent infrastructures : 8th EAI international conference, FABULOUS 2024 : Zagreb, Croatia, May 9–10, 2024 : proceedings / eds. Dragan Perakovic, Lucia Knapcikova. — Cham : Springer Nature Switzeland, cop. 2025. — ( Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering ; ISSN 1867-8211 ; vol. 596 ). — ISBN: 978-3-031-72392-6; e-ISBN: 978-3-031-72393-3. — S. 3–8. — Bibliogr., Abstr. — Publikacja dostępna online od: 2024-10-16. — P. A. Kowalski, Sz. Łukasik, P. Kulczycki - dod. afiliacja: Polish Academy of Sciences, Systems Research Institute, Warsaw
Autorzy (6)
- AGHKowalski Piotr Andrzej
- AGHJęczmionek Ernest
- Charytanowicz Małgorzata
- AGHŁukasik Szymon
- Niewczas Jerzy
- AGHKulczycki Piotr
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 158455 |
|---|---|
| Data dodania do BaDAP | 2025-03-04 |
| DOI | 10.1007/978-3-031-72393-3_1 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Czasopismo/seria | Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering |
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.
