A visual dataset for recognition of rice varieties.

Computer vision Deep learning Image classification Rice varieties

Journal

Data in brief
ISSN: 2352-3409
Titre abrégé: Data Brief
Pays: Netherlands
ID NLM: 101654995

Informations de publication

Date de publication:
Jun 2024
Historique:
received: 08 12 2023
revised: 05 04 2024
accepted: 12 04 2024
medline: 7 5 2024
pubmed: 7 5 2024
entrez: 7 5 2024
Statut: epublish

Résumé

This article presents a comprehensive dataset sourced from various markets across Bangladesh, highlighting 20 distinct rice varieties predominantly consumed locally. The dataset encompasses a diverse range of rice strains, including Subol Lota, Bashmoti (Deshi), Ganjiya, Shampakatari, Sugandhi Katarivog, BR-28, BR-29, Paijam, Bashful, Lal Aush, BR-Jirashail, Gutisharna, Birui, Najirshail, Pahari Birui, Polao (Katari), Polao (Chinigura), Amon, Shorna-5, and Lal Binni. Using a smartphone camera, low-resolution images capturing the essence of each rice variety were meticulously obtained, resulting in a total of 4,730 images with a non-uniform distribution. The dataset also includes augmented data, totaling 23,650 images. This precisely curated dataset holds significant promise and utility, showcasing diverse attributes, including the unique representation of 20 rice varieties, each characterized by distinct colors, sizes, and potential applications within the agricultural sector.

Identifiants

pubmed: 38711738
doi: 10.1016/j.dib.2024.110442
pii: S2352-3409(24)00411-6
pmc: PMC11070660
doi:

Types de publication

Journal Article

Langues

eng

Pagination

110442

Informations de copyright

© 2024 The Author(s).

Auteurs

Md Masudul Islam (MM)

Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh.

Galib Muhammad Shahriar Himel (GMS)

Department of Physics, Jahangirnagar University, Dhaka, Bangladesh.

Mohammad Shorif Uddin (MS)

Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh.

Md Golam Moazzam (MG)

Department of Computer Science and Engineering, Jahangirnagar University, Dhaka, Bangladesh.

Classifications MeSH