Evidence map›Paper›PMID 40115614›Full record

ArticleData in brief2025

UDCAD-DFL-DL: A unique dataset for classifying and detecting agricultural diseases in dragon fruits and leaves.

Pronob Chandra Sarkar, Gourab Kumar Pranta, Mayen Uddin Mojumdar, Arif Mahmud, Sheak Rashed Haider Noori, Narayan Ranjan Chakraborty

Abstract read
PubMed Full text at PMC Publisher
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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

6 authors.

Pronob Chandra SarkarMultidisciplinary Action Research Laboratory, Department of Computer Science and Engineering, Daffodil International University, Birulia, Dhaka 1216, Bangladesh.
Gourab Kumar PrantaMultidisciplinary Action Research Laboratory, Department of Computer Science and Engineering, Daffodil International University, Birulia, Dhaka 1216, Bangladesh.
Mayen Uddin MojumdarMultidisciplinary Action Research Laboratory, Department of Computer Science and Engineering, Daffodil International University, Birulia, Dhaka 1216, Bangladesh.
Arif MahmudMultidisciplinary Action Research Laboratory, Department of Computer Science and Engineering, Daffodil International University, Birulia, Dhaka 1216, Bangladesh.
Sheak Rashed Haider NooriMultidisciplinary Action Research Laboratory, Department of Computer Science and Engineering, Daffodil International University, Birulia, Dhaka 1216, Bangladesh.
Narayan Ranjan ChakrabortyMultidisciplinary Action Research Laboratory, Department of Computer Science and Engineering, Daffodil International University, Birulia, Dhaka 1216, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Leaf and fruit diseases are unavoidable due to the unpredictability of climate variability and environmental changes. This study presents dragon fruit and leaf diseases dataset. The dataset includes a total of 4,518 clear and detailed images. The dataset lists the following diseases of leaf and fruit along with the number of images for each: Bacterial Diseases (498), Fungal Infections (Anthracnose or Stem Canker) (84), Healthy Leaves (2,242), Sunburn Damage (271), Healthy Fruits (333), Insect Infected Fruits (53), Mealybugs and Scale Insects (Fruits) (173), Bacterial Wilt (Fruits) (84). All images were captured with two smartphones one is Google Pixel 7 Pro and another one is iPhone 14 Pro in the Dhaka Division. This dataset focuses on analysing the diseases of Dragon leaf and fruit to improving the agriculture sector.

Indexed as

AgricultureBacterial diseasesComputer visionDragon fruitLeaf diseasesStem cankerSunburn damage

Identifiers

PMID40115614
PMCPMC11925099

What OpenQuestion holds

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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.