Facilitating spice recognition and classification: An image dataset of Indian spices

IF 1 Q3 MULTIDISCIPLINARY SCIENCES
Sandip Thite , Deepali Godse , Kailas Patil , Prawit Chumchu , Alfa Nyandoro
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引用次数: 0

Abstract

This data paper presents a comprehensive visual dataset of 19 distinct types of Indian spices, consisting of high-quality images meticulously curated to facilitate various research and educational applications. The dataset includes extensive imagery of the following spices: Asafoetida, Bay Leaf, Black Cardamom, Black Pepper, Caraway Seeds, Cinnamon Stick, Cloves, Coriander Seeds, Cubeb Pepper, Cumin Seeds, Dry Ginger, Dry Red Chilly, Fennel Seeds, Green Cardamom, Mace, Nutmeg, Poppy Seeds, Star Anise, and Stone Flowers. Each image in the dataset has been captured under controlled conditions to ensure consistency and clarity, making it an invaluable resource for studies in food science, agriculture, and culinary arts. The dataset can also support machine learning and computer vision applications, such as spice recognition and classification. By providing detailed visual documentation, this dataset aims to promote a deeper understanding and appreciation of the rich diversity of Indian spices.
促进香料识别和分类:印度香料图像数据集
这篇数据论文展示了一个包含 19 种不同印度香料的综合可视化数据集,该数据集由精心策划的高质量图像组成,以促进各种研究和教育应用。数据集包括以下香料的大量图像:阿苏、贝叶、黑豆蔻、黑胡椒、香芹籽、肉桂棒、丁香、芫荽籽、库比胡椒、小茴香籽、干姜、干红辣椒、茴香籽、绿豆蔻、肉豆蔻、肉豆蔻、罂粟籽、八角和石花。数据集中的每张图像都是在受控条件下采集的,以确保一致性和清晰度,因此是食品科学、农业和烹饪艺术研究的宝贵资源。该数据集还可支持机器学习和计算机视觉应用,如香料识别和分类。通过提供详细的视觉记录,该数据集旨在促进人们更深入地了解和欣赏印度香料的丰富多样性。
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
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.
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