Evidence map›Paper›PMID 41018862›Full record

ArticleData in brief2025

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

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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Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Md Sajedur RahmanDepartment of Computer Science and Engineering, Bangladesh University of Business and Technology, Mirpur-2, Dhaka 1216, Bangladesh.
Md Mahfuz Ahmed NahinDepartment of Computer Science and Engineering, Bangladesh University of Business and Technology, Mirpur-2, Dhaka 1216, Bangladesh.
Md Mahbubur RahmanDepartment of Computer Science and Engineering, Bangladesh University of Business and Technology, Mirpur-2, Dhaka 1216, Bangladesh.
Mollika RaniDepartment of Computer Science and Engineering, Bangladesh University of Business and Technology, Mirpur-2, Dhaka 1216, Bangladesh.
Md Ashraful IslamDepartment of Computer Science and Engineering, Bangladesh University of Business and Technology, Mirpur-2, Dhaka 1216, Bangladesh.
Al BashirDepartment of Biological & Agricultural Engineering, Texas A&M University, College Station, TX, 77843, USA.
Ahmad ShafkatDepartment of Computer Science and Engineering, Bangladesh University of Business and Technology, Mirpur-2, Dhaka 1216, Bangladesh.
Bijon MallikDepartment of Computer Science and Engineering, Bangladesh University of Business and Technology, Mirpur-2, Dhaka 1216, Bangladesh.
Yaqoob MajeedDepartment of Electrical Engineering and Computer Science (EECS), University of Wyoming, Laramie, WY, 82071, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Bangladeshi mango cultivarsComputer visionFruit recognitionImage datasetMachine learningMangoclassify-12 dataset

Identifiers

PMID41018862
PMCPMC12475408

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.