Evidence map›Paper›PMID 40322505›Full record

ArticleData in brief2025

ShrimpDiseaseBD: An image dataset for detecting shrimp diseases in the aquaculture sector of Bangladesh.

Mohammad Manzurul Islam, Anabil Sarker, Ashiquzzaman Choudhury, Noortaz Ahmed, Ahmed Abdal Shafi, Nishat Tasnim Niloy, Md Shorif Hossain, Md Sawkat Ali, Abdullahi Chowdhury, Md Hasanul Ferdaus

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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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

1 citing paper in PubMed.

  1. Article
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

10 authors.

Mohammad Manzurul IslamDepartment of Computer Science and Engineering, East West University, Dhaka 1212, Bangladesh.
Anabil SarkerDepartment of Computer Science and Engineering, East West University, Dhaka 1212, Bangladesh.
Ashiquzzaman ChoudhuryDepartment of Computer Science and Engineering, East West University, Dhaka 1212, Bangladesh.
Noortaz AhmedDepartment of Computer Science and Engineering, East West University, Dhaka 1212, Bangladesh.
Ahmed Abdal ShafiDepartment of Computer Science and Engineering, East West University, Dhaka 1212, Bangladesh.
Nishat Tasnim NiloyDepartment of Computer Science and Engineering, East West University, Dhaka 1212, Bangladesh.
Md Shorif HossainDepartment of Computer Science and Engineering, East West University, Dhaka 1212, Bangladesh.
Md Sawkat AliDepartment of Computer Science and Engineering, East West University, Dhaka 1212, Bangladesh.
Abdullahi ChowdhuryDepartment of Computer Science and Engineering, East West University, Dhaka 1212, Bangladesh.
Md Hasanul FerdausDepartment of Computer Science and Engineering, East West University, Dhaka 1212, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Shrimp farming is a significant contributor to Bangladesh's economy, providing livelihoods for millions of people in coastal areas. However, the shrimp industry faces challenges from prevalent shrimp diseases, which can disrupt the economy and harm the environment. Detecting these diseases early and effectively is crucial. To address this concern, a dataset has been developed containing images of healthy and diseased shrimp of different types. The images were collected from local shrimp farms under expert supervision using high-quality smartphone cameras. The dataset includes 1149 original images, with diseased shrimp images annotated to improve detection capabilities. This dataset is expected to be valuable for detecting shrimp diseases with precision and timing and is likely to encourage research and practical applications in automated shrimp health monitoring. It will also be a valuable resource for computer vision and aquaculture researchers.

Indexed as

AquacultureAutomated detectionComputer visionShrimp disease detectionShrimp health monitoring

Identifiers

PMID40322505
PMCPMC12048804

What OpenQuestion holds

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LicenceCC BY-NC
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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.