Lesion-Aware Multiple-Instance Learning for Grapevine Disease Recognition from Field RGB Images.

Övünç Öztürk, Bora Canbula, Tuğba Özacar, Vehbi Özacar

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Övünç ÖztürkDepartment of Computer Engineering, Manisa Celal Bayar University, Şehit Prof. Dr. İlhan Varank Campus, Manisa 45140, Türkiye.ORCID 0000-0001-7127-7902

Bora CanbulaDepartment of Computer Engineering, Manisa Celal Bayar University, Şehit Prof. Dr. İlhan Varank Campus, Manisa 45140, Türkiye.ORCID 0000-0003-1088-2804

Tuğba ÖzacarDepartment of Computer Engineering, Manisa Celal Bayar University, Şehit Prof. Dr. İlhan Varank Campus, Manisa 45140, Türkiye.

Vehbi ÖzacarOctahedron Innovation Informatics Electronics and Mining Services Inc., Manisa Technopark, Manisa 45140, Türkiye.ORCID 0000-0002-5842-8777

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This retrospective offline study examines how dense manual regional annotations can be organized for image-level grapevine disease recognition using pre-acquired HERMOS RGB images. After audit and filtering, 491 source images and 13,765 Pascal VOC regions were retained. A leakage-safe source-image split was used across full-image classification, regional crop classification, crop-to-image aggregation, annotation-guided multiple-instance learning (MIL), and YOLO11 localization. MIL bags in training, validation, and testing were constructed from crops derived from manual boxes; the results therefore describe annotation-assisted aggregation rather than raw-image deployment. The original ConvNeXt-Tiny MIL model achieved macro-F1 0.922 (95% source-image bootstrap CI 0.882-0.956), compared with 0.836 (0.754-0.895) for full-image ConvNeXt-Tiny and 0.869 (0.804-0.917) for the best fixed crop aggregation. Paired macro-F1 differences favored MIL over these baselines by 0.087 (95% CI 0.029-0.163; Holm-adjusted

Indexed as

Image Processing, Computer-AssistedPlant DiseasesVitisMultiple-Instance Learning Algorithmsablation studyannotation-guided learningbootstrap uncertaintygrapevine disease recognitionlesion detectionmultiple-instance learningoffline image analysis

Identifiers

PMID42884519

DOI10.3390/s26196318

PMCPMC13667579

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

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.