SpiceSpectrum: Class-balanced dataset of commercially valuable spice cultivars

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief Pub Date : 2025-09-20 DOI:10.1016/j.dib.2025.112097

Md. Noushad Jahan Ramim, Samira Islam, Muhtasin Towkir, Md Mubtasim Fuad, Noor Mairukh Khan Arnob

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引用次数: 0

Abstract

This data paper introduces a comprehensive visual dataset of 11 globally significant spices, comprising 11,000 high-quality images curated to support research and applications in agriculture, trade, and culinary arts. The dataset features the following spices: Black Pepper, Cardamom, Cinnamon, Cloves, Coriander, Cumin, Ginger, Nutmeg, Paprika, Saffron, and Turmeric, selected for their economic importance, medicinal value, and widespread culinary use across global cuisines. Images were collected from Bangladesh, capturing diverse lighting conditions, backgrounds, natural textures, packaging styles, and multiple angles to reflect real-world scenarios. Stored in JPG format at a standardized 512 × 512 pixel resolution, the dataset is organized into dedicated folders with 1000 images per spice, ensuring consistency and ease of use. This resource is designed to facilitate machine learning and computer vision applications, such as spice recognition, classification, and quality assessment, as well as AI-driven tools for agricultural analysis and trade monitoring. By providing a robust and diverse visual dataset, this collection aims to advance research and to assist innovation in the global spice industry.

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spicspectrum：具有商业价值的香料品种的类别平衡数据集

本数据文件介绍了11种全球重要香料的综合视觉数据集，包括11,000个高质量图像，以支持农业，贸易和烹饪艺术的研究和应用。该数据集的特点是以下香料：黑胡椒、小豆蔻、肉桂、丁香、香菜、小茴香、姜、肉豆蔻、红辣椒、藏红花和姜黄，选择这些香料是因为它们的经济重要性、药用价值和在全球美食中的广泛烹饪用途。从孟加拉国收集的图像捕捉了不同的照明条件、背景、自然纹理、包装风格和多个角度，以反映现实世界的场景。以标准化的512 × 512像素分辨率的JPG格式存储，数据集被组织到专用文件夹中，每个香料有1000张图像，确保一致性和易用性。该资源旨在促进机器学习和计算机视觉应用，如香料识别、分类和质量评估，以及用于农业分析和贸易监测的人工智能驱动工具。通过提供一个强大的和多样化的视觉数据集，该集合旨在推进研究和协助创新在全球香料行业。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Data in Brief MULTIDISCIPLINARY SCIENCES-

CiteScore

3.10

自引率

0.00%

发文量

996

审稿时长

70 days

期刊介绍： Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.