Method / resourcemodel-written summary

YOLO11 trained on the dataset shows state-of-the-art performance in identifying and segmenting overlapping soybean and cotton foliage.

A claim from A Leaf-Level Dataset for Soybean-Cotton Detection and Segmentation · 2026

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

YOLO11 trained on the dataset shows state-of-the-art performance in identifying and segmenting overlapping soybean and cotton foliage.

Taxon
Soybean and cottonGossypium hirsutumGlycine maxe0
Outcome
identifying and segmenting overlapping foliagee3
Comparator
state-of-the-arte3

Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSettingDirectionValueQualifier

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

Gossypium hirsutum
Gossypium hirsutum · TaxonChriKo · CC BY-SA 3.0 · Wikimedia Commons ↗
Glycine max
Glycine max · TaxonPublic domain · Wikimedia Commons ↗

Verbatim

e0

“Soybean and cotton are major drivers of many countries' agricultural sectors”

✓ located verbatim in the source · abstract, characters 0–76
e3

“We validate this dataset using YOLO11, demonstrating state-of-the-art performance in accurately identifying and segmenting overlapping foliage”

✓ located verbatim in the source · abstract, characters 944–1086

Title

A Leaf-Level Dataset for Soybean-Cotton Detection and Segmentation

Thiago H. Segreto; Juliano D. Negri; Paulo H. Polegato; et al. Scientific Data 2026. doi:10.1038/s41597-026-07092-8

Other claims from this paper (3)

Record rev-33778f3fe146c63c5af7b50c67dc64c7911f03d068892bf6963fce7a18b9d433 · schema claims-v0.9 · position 4 in the model’s answer