Evidence map›Paper›PMID 40231149›Full record

ArticleData in brief2025

Banana bunch image and video dataset for variety classification and grading.

D S Guru, Saritha N

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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2 · The registry

The trial behind it

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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

2 authors.

D S GuruDepartment of Studies in Computer Science, University of Mysore, Manasagangotri, Mysuru, Karnataka, 570006, India.
Saritha NDepartment of Studies in Computer Science, University of Mysore, Manasagangotri, Mysuru, Karnataka, 570006, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Banana, a major commercial fruit crop, holds high nutritional value and widespread consumption [[4], [8],10]. The global banana market valued at USD 140.83 billion in 2024 is projected to reach USD 147.74 billion by 2030. Accurate variety identification and quality grading are crucial for marketing, pricing, and operational efficiency in food processing industries [9]. As wholesalers and food processing industries process bananas in bunches (not individual fruit levels) , our bunch-level dataset offers a more accurate assessment by capturing bunch-level characteristics, which are vital for grading. Existing datasets, such as [1,6], focus on individual bananas or have limited bunch-level data, highlighting the lack of large-scale bunch datasets. This dataset fills the gap by providing bunch-level images and videos of three widely consumed banana varieties-Elakki-bale, Pachbale, and Rasbale, from Mysuru, South Karnataka, India, serving as a valuable resource for food processing industries. Our dataset supports training machine learning models for bunch-level variety classification and grading of bananas and serves as a resource for research and education.

Indexed as

Artificial intelligenceBanana bunch-level datasetBanana gradingBanana variety classificationComputer visionFood scienceMachine learningPrecision agriculture

Identifiers

PMID40231149
PMCPMC11994905

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.