Evidence map›Paper›PMID 42272828›Full record

ArticleData in brief2026

SAR-MLD1-2025-MangoLeaf: A comprehensive high-quality mango leaf dataset for disease classification.

Md Robiul Awoal, Afjal H Sarower, Md Zubair Islam, Sharmin Akhter, Md Jubayer Hossain, S M Aminul Haque, Sheak Rashed Haider Noori

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In one paragraph

Article in Data in brief, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

7 authors.

Md Robiul AwoalDepartment of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.
Afjal H SarowerDepartment of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.
Md Zubair IslamDepartment of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.
Sharmin AkhterSAR Lab, Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.
Md Jubayer HossainDepartment of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.
S M Aminul HaqueDepartment of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.
Sheak Rashed Haider NooriDepartment of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This article presents a carefully curated dataset of mango leaves, collected from the northern regions of Bangladesh, specifically from Naogaon District, a major mango-producing area. Bangladesh, known for its agriculture, frequently faces leaf diseases that impact mango yield and quality. Early detection is crucial to prevent widespread damage and ensure better disease management. The dataset comprises 4921 raw image samples, categorized into five distinct classes: Healthy, Anthracnose, Powdery Mildew, Turning Brown, and Gall Midge. The images were captured under natural lighting conditions to preserve the leaves' intrinsic visual features, ensuring authenticity and variability. This dataset is a valuable resource for botanical research and machine learning applications, particularly in the automated classification of mango leaf diseases, helping researchers develop more effective disease detection models.

Indexed as

Computer visionDisease classificationMachine learningMango leaf

Identifiers

PMID42272828
PMCPMC13247580

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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.