The improved extreme random tree reached 87.425% F1-Score.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Taxon: maizeZea mayse2
Outcome: F1-Scorsee6
Value: 87.425%e6

Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirectionQualifier

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

*Zea mays* · TaxonAkwugo · CC BY-SA 4.0 · Wikimedia Commons ↗

Verbatim

e6
“the F1-Scorse has been 87.425%”
✓ located verbatim in the source · abstract, characters 1889–1919

e2
“maize seeds were collected from the test site”
✓ located verbatim in the source · abstract, characters 487–532

Title

SSCBKA-optimized machine learning for hyperspectral classification of maize seeds for agricultural quality control

Shizheng Qu; Xuena Geng; Zhuoshi Li Smart Agricultural Technology 2026. doi:10.1016/j.atech.2025.101771

Other claims from this paper (8)

Method / resource The improved algorithm converged fastest and optimized best compared with other algorithms.
Method / resource The improved extreme random tree reached 88.268% accuracy.
Method / resource The improved extreme random tree reached 94.805% precision.
Method / resource The improved extreme random tree reached 81.100% recall.
Method / resource The improved random forest reached 89.385% accuracy.
Method / resource The improved random forest reached 88.172% precision.
Method / resource The improved random forest reached 91.100% recall.
Method / resource The improved random forest reached 89.617% F1-Score.

Record rev-3ca8b0fd697cafd44a4d50299bb664b00f5f340c89972159a84535412efc364c · schema claims-v0.9 · position 5 in the model’s answer