Evidence map›Paper›PMID 40470337›Full record

ArticleData in brief2025

Comprehensive smartphone image dataset for Aegle Marmelos, Hog plum, and lemon plant leaf disease and freshness assessment.

Mohammad Rezwanul Huq, Jubaer Ahmed, Raiyan Gani, Maherun Nessa Isty, Tasmia Islam

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 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

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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

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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

5 authors.

Mohammad Rezwanul HuqDepartment of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.
Jubaer AhmedDepartment of Computer Science and Engineering, East West University, Aftabnagar, Dhaka, Bangladesh.
Raiyan GaniDepartment 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.
Tasmia IslamDepartment 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

Fruits, which are packed with nutrients, vitamins, and antioxidants, have been known for their numerous health benefits and curative powers, and are utilized in conventional medicine. Aegle Marmelos, Lemon, and Hog Plum are tangy fruits widely recognized in Asian countries for containing a plentiful supply of bioactive substances. They are also highly valuable in boosting metabolism, possessing tremendous therapeutic properties, and holding financial significance. The leaves of these fruit trees are as essential as their fruits, as they contain versatile medicinal and dietary benefits of immense value. However, these leaves are often affected by various fungal and other diseases, which reduce the ability for healthy growth and productivity of both fruits and leaves. Plants infected with various leaf diseases can produce fewer fruits, which are also of lower quality due to failure to reach maturity and lack of sufficient nutritional value. For these reasons, there is a risk of an outbreak in orchards, which can lead to significant financial losses for both producers and the agricultural sector. This signifies that the early identification of leaf diseases and the management of orchards are essential to minimize the impact of leaf diseases and mitigate these issues, ensuring the healthy production of valuable medicinal fruits. In this paper, various infected leaf images are collected from different regions of Rangpur, providing a comprehensive dataset comprising 3941 images. The dataset includes images of three different plant leaves, where 1513 images of Aegle Marmelos, 1232 images of Lemon, and 1196 of Hog plum, where each of the categories encompasses several classes of common leaf diseases. Through this dataset, an early and accurate digital detection system can be employed, allowing producers to clearly identify diseases instead of relying on traditional methods. The precise and timely identification of leaf diseases enables the control of these diseases by taking necessary actions, ensuring the sustainability of plants, and promoting the healthy growth of these invaluable medicinal fruits.

Indexed as

Aegle Marmelos leaf diseaseAgricultural challengesFruit leaf disease identificationHog plum leaf diseaseLemon leaf disease

Identifiers

PMID40470337
PMCPMC12136706

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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.