芒果分类-12:12个孟加拉国本土芒果品种的高分辨率图像数据集

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief Pub Date : 2025-09-07 DOI:10.1016/j.dib.2025.112037

Md Sajedur Rahman , Md Mahfuz Ahmed Nahin , Md Mahbubur Rahman , Mollika Rani , Md Ashraful Islam , Al Bashir , Ahmad Shafkat , Bijon Mallik , Yaqoob Majeed

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引用次数: 0

摘要

一个高分辨率的图像数据集MangoClassify-12，包括3900张JPEG图像的12个孟加拉国本土芒果品种，组装以实现自动分类。这些图像是在2025年5月初至7月10日期间使用四部智能手机在自然光下从三个不同的生产区（米尔普尔-2，达卡；Phulbari, Dinajpur; Rajshahi）拍摄的。所有的图像都经过农业专家的审查，以排除损坏或过熟的标本。该数据集涵盖了12个品种：Amrapali、Himsagar、Harivanga、langa、Fazli、Gopalbhog、Ranibhog、Gobindobhog、Sundari、Banana Mango、Bari-4和Khirsapat。元数据以结构化的文件夹层次结构组织。MangoClassify-12可以通过DOI公开访问，并支持机器学习应用程序，如品种识别、质量评估和基于移动的识别。通过提供没有预定义分割或增强的原始图像，该数据集为农业计算机视觉研究提供了一个灵活的基准。

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MangoClassify-12: A high-resolution image dataset of twelve indigenous Bangladeshi mango cultivars

A high-resolution image dataset, MangoClassify-12, comprising 3900 JPEG images of twelve indigenous Bangladeshi mango cultivars, was assembled to enable automated classification. Images were captured between early May and July 10th, 2025, from three distinct production regions (Mirpur-2, Dhaka; Phulbari, Dinajpur; Rajshahi) under natural light using four smartphones. All images were reviewed by agricultural experts to exclude damaged or overripe specimens. The dataset covers twelve cultivars: Amrapali, Himsagar, Harivanga, Langra, Fazli, Gopalbhog, Ranibhog, Gobindobhog, Sundari, Banana Mango, Bari-4 and Khirsapat. Metadata are organized in a structured folder hierarchy. MangoClassify-12 is openly accessible via DOI and supports machine learning applications such as variety identification, quality assessment and mobile-based recognition. By providing raw images without predefined splits or augmentations, the dataset offers a flexible benchmark for computer vision research in agriculture.

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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.