Evidence map›Paper›PMID 41210364›Full record

ArticleData in brief2025

MoringaLeafNet: A multi-class leaf disease dataset for precision agriculture and deep learning research.

Sabit Ahamed Preanto, Tapon Paul, Abid Khan, Md Hasan Imam Bijoy

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. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Sabit Ahamed PreantoDepartment of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka 1216, Bangladesh.
Tapon PaulDepartment of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka 1216, Bangladesh.
Abid KhanDepartment of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka 1216, Bangladesh.
Md Hasan Imam BijoyDepartment of Computer Science and Engineering, Daffodil International University, Daffodil Smart City, Birulia, Dhaka 1216, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Moringa Oleifera, which has outstanding nutritional and health benefits, is prized around the world because its leaves are rich in essential vitamins, antioxidants, and minerals that support digestion, help the immune system, and fight inflammation. Still, growing Moringa can be difficult because diseases such as Yellow Leaf, Bacterial Leaf Spot, and Cercospora Leaf Spot are hard to detect early and spread fast, leading to a lot of damage. These illnesses cause plants to make less yield, so farmers depend on pesticides and spend more, which also damages the environment and their crops. Here, we make available the MoringaLeafNet dataset, including high-quality images of leaves from the Moringa tree affected by different diseases. The images in the dataset, gathered from March to April and August to September 2025, are divided into four classes: Healthy Leaf, Yellow Leaf, Bacterial Leaf Spot, and Cercospora Leaf Spot. We collected images from Sumi Nursery in Madhupur, Tangail, Bangladesh, and Rafin Nursery in Birulia, Savar, Bangladesh, under various weather conditions. To facilitate better use in deep learning, random rotation, flipping, and brightness/contrast adjustments were performed on the data. The dataset will help develop new disease detection systems in agriculture that allow the early recognition of Moringa leaf diseases. It can also support the development of real-time diagnostic systems that provide farmers with timely insights for decision-making.

Indexed as

Computer vision in agricultureDetecting leaf diseaseLeaf classificationMoringa leaf diseaseSmart AgricultureSustainable Agriculture

Identifiers

PMID41210364
PMCPMC12595119

What Socratic holds

Textmetadata
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 Socratic graph.