Adding thermal infrared imagery further improved the maize phenology monitoring accurac…

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Adding thermal infrared imagery further improved the maize phenology monitoring accuracy to 97.5%.

Taxon: MaizeZea mayst0
Outcome: accuracye1
Direction: improvede1
Value: 97.5%e1

Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingQualifier

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

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

Verbatim

e1
“the inclusion of thermal infrared imagery further improved the accuracy to 97.5%”
✓ located verbatim in the source · abstract, characters 1558–1638

t0
“Maize Phenology”
✓ located verbatim in the source · title, characters 11–26

Title

Monitoring Maize Phenology Using Multi-Source Data by Integrating Convolutional Neural Networks and Transformers

Yugeng Guo; Wenzhi Zeng; Haoze Zhang; et al. Remote Sensing 2026. doi:10.3390/rs18020356

Other claims from this paper (1)

Method / resource Integrating multispectral imagery and accumulated temperature in the CNN-Transformer phenology monitoring model for maize achieved an accuracy of 92.9%.

Record rev-5df0108cba39da36c02fed7048cc18ffd689f16657adbcbc313ef7df15f16244 · schema claims-v0.9 · position 2 in the model’s answer