Evidence map›Paper›PMID 39850369›Full record

ArticleData in brief2025

Smartphone image dataset for radish plant leaf disease classification from Bangladesh.

Mahamudul Hasan, Raiyan Gani, Mohammad Rifat Ahmmad Rashid, Maherun Nessa Isty, Raka Kamara, Taslima Khan Tarin

Abstract read
In one paragraph

Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Mahamudul HasanDepartment of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.
Raiyan GaniDepartment of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.
Mohammad Rifat Ahmmad RashidDepartment of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.
Maherun Nessa IstyDepartment of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.
Raka KamaraDepartment of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.
Taslima Khan TarinDepartment of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Radishes, which are common root vegetables, are rich in vitamins and minerals, and contain low calories. This vegetable is known for its rapid growth. Nevertheless, the variety of leaf diseases where leaves get affected by various bacterial and fungal diseases can hinder the healthy growth of radish. Furthermore, there is a high risk of inaccurate identification of diseases if the farmers try to use traditional methods in recognizing these diseases. With the purpose of precise identification of radish leaf diseases for the finest growth of this vegetable, total of 2801 images of the radish leaves are collected from vegetable field in Bangladesh. The collected dataset includes comprehensive images of healthy leaves as well as four types of leaf affected by various diseases such as Black Leaf Spot, Downey Mildew, Flea Beetle and Mosaic. Utilizing this robust dataset, deep learning models can be trained to identify the leaf diseases which helps to detect the diseases in order to reduce the harm of the cultivation of radish. By identifying the diseases on radish leaves accurat-ely and maintaining healthy production of radish, this dataset contributes to the broader sustainability in the agricultural sector.

Indexed as

Agricultural challengesDataset collectionDisease identificationImage analysisLeaf diseasesRadish plant leaf disease

Identifiers

PMID39850369
PMCPMC11754492

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LicenceCC BY
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Registered trials

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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.