The detection method achieved 83.7% mean average precision.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Taxon: rapeseedt1
Interactor: Brassicogethes aeneusBrassicogethes aeneust0pest
Outcome: mean average precision (mAP)e0
Value: 83.7%e0

Not reportedMaterialGeneCompoundOrgan / stageSecond interactorSecond interactor roleTreatmentComparatorSettingDirectionQualifier

Organisms pictured · Representative pictures of the matched species, not figures from the paper.

*Brassicogethes aeneus* · Interactor · picture from its Wikimedia Commons categoryUdo Schmidt from Deutschland · CC BY-SA 2.0 · Wikimedia Commons ↗

Verbatim

e0
“the proposed method achieved a mean average precision (mAP) of 83.7% on the test dataset”
✓ located verbatim in the source · abstract, characters 1119–1207

t0
“Brassicogethes aeneus in Oilseed Rape”
✓ located verbatim in the source · title, characters 41–78

t1
“rapeseed crops”
✓ located verbatim in the source · abstract, characters 947–961

Title

Deep Learning for Real-Time Detection of Brassicogethes aeneus in Oilseed Rape Using the YOLOv4 Architecture

Ziemowit Malecha; Kajetan Ożarowski; Rafał Siemasz; et al. Applied Sciences 2026. doi:10.3390/app16021075

Other claims from this paper (2)

Method / resource Field measurements produced density maps of pollen beetle distribution.
Method / resource The maps demonstrated potential for pesticide savings.

Record rev-18d832ff5fda8e271aa17e158e4e3ad989f862dfa8dff8bd5c3fc3ca6c5fa1e5 · schema claims-v0.9 · position 1 in the model’s answer