Evidence map›Paper›PMID 42643448›Full record

ArticleFrontiers in plant science2026

Backbone diversity beats text supervision: a systematic study of frozen multi-foundation model fusion for in-the-wild plant disease recognition.

Hajar El Karch, Youssef Natij, Mohamed Benaly, Rachid El Gouri, Abdelkader Mezouari

Abstract read
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Article in Frontiers in plant science, 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

5 authors.

Hajar El KarchLaboratory of Advanced Systems Engineering, National School of Applied Engineering, Ibn Tofail University, Kenitra, Morocco.
Youssef NatijScientific Research and Innovation Laboratory, Higher School of Technology, Ibn Tofail University, Kenitra, Morocco.
Mohamed BenalyInnovative Systems Engineering Laboratory, National School of Applied Sciences of Tetouan, Abdelmalek Essaadi University, Tetouan, Morocco.
Rachid El GouriLaboratory of Advanced Systems Engineering, National School of Applied Engineering, Ibn Tofail University, Kenitra, Morocco.
Abdelkader MezouariScientific Research and Innovation Laboratory, Higher School of Technology, Ibn Tofail University, Kenitra, Morocco.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Automated plant disease recognition from field photographs remains challenging: models trained on laboratory datasets collapse on in-the-wild images, and the current state of the art on PlantWild-the largest open in-the-wild benchmark (18,542 images, 89 classes)-relies on text prototypes derived from a language model to reach 76.18% top 1 accuracy. We ask whether text supervision is truly necessary or whether the bottleneck is the

Indexed as

feature fusionfoundation modelsin-the-wild benchmarksplant disease recognitionPlantDocPlantWildprototypical classificationself-supervised vision transformers

Identifiers

PMID42643448
PMCPMC13503617

What OpenQuestion holds

Textmetadata
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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.