Evidence map›Paper›PMID 41341315›Full record

ArticleFrontiers in plant science2025

Species-level detection of thrips and whiteflies on yellow sticky traps using YOLO-based deep learning detection models.

Broes Laekeman, Jochem Bonte, Wannes Dermauw, Annelies Christiaens, Bruno Gobin, Johan Van Huylenbroeck, Emmy Dhooghe, Peter Lootens

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In one paragraph

Article in Frontiers in plant science, 2025. 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
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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

8 authors.

Broes LaekemanPlant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), Merelbeke-Melle, Belgium.
Jochem BontePlant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), Merelbeke-Melle, Belgium.
Wannes DermauwPlant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), Merelbeke-Melle, Belgium.
Annelies ChristiaensViaverda, Destelbergen, Belgium.
Bruno GobinViaverda, Destelbergen, Belgium.
Johan Van HuylenbroeckPlant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), Merelbeke-Melle, Belgium.
Emmy DhoogheDepartment of Plants and Crops, Faculty of Bioscience Engineering, Ghent University (UGent), Gent, Belgium.
Peter LootensPlant Sciences Unit, Flanders Research Institute for Agriculture, Fisheries and Food (ILVO), Merelbeke-Melle, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As of today, pest insects such as thrips and whiteflies cause the loss of 20% - 40% of the global agricultural yield. To reduce chemical pesticide use while maintaining high-quality horticultural standards, early detection of pest infestations is essential. Although AI-assisted pest monitoring systems using sticky trap images exist today, none currently enable effective species-level detection of thrips and/or whiteflies. However, early species-level identification would allow for more targeted, species-specific control strategies, leading to reduced, localized, and more efficient pesticide application. Therefore, in this study, we evaluated the potential and limitations of real-time species-level detection of thrips (

Indexed as

artificial intelligence (AI)automated pest monitoringBemisia tabaciEchinothrips americanusFrankliniella occidentalisintegrated pest management (IPM)smart trapsTrialeurodes vaporariorum

Identifiers

PMID41341315
PMCPMC12669111

What OpenQuestion holds

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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 OpenQuestion graph.