Method / resourcemodel-written summary

In five-year forecasting the model reduced RMSE by 3.2914% relative to leading models such as AMAP on US soybean county data.

A claim from Enhancing Agricultural Sustainability Through Efficient Yield Monitoring: A Lightweight Spatio-Temporal Fusion Model with Dual Symmetric Branches · 2026

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

In five-year forecasting the model reduced RMSE by 3.2914% relative to leading models such as AMAP on US soybean county data.

Taxon
soybeanGlycine maxe1
Outcome
RMSEe0
Direction
reducese0
Value
3.2914%e0
Comparator
leading models like AMAPe2
Setting
major U.S. soybean-producing statese1

Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentQualifier

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

Glycine max
Glycine max · TaxonPublic domain · Wikimedia Commons ↗

Verbatim

e0

“For five-year long-term forecasting, it reduces the RMSE by 3.2914% and increases the R2 by 4.7537%”

✓ located verbatim in the source · abstract, characters 1457–1556
e1

“major U.S. soybean-producing states”

✓ located verbatim in the source · abstract, characters 1176–1211
e2

“compared with leading models like AMAP”

✓ located verbatim in the source · abstract, characters 1257–1295

Title

Enhancing Agricultural Sustainability Through Efficient Yield Monitoring: A Lightweight Spatio-Temporal Fusion Model with Dual Symmetric Branches

Fengling Li; Chengjun Xu; Ronghua Jin Sustainability 2026. doi:10.3390/su18052418

Other claims from this paper (3)

Record rev-07c399a7dc4cb5592fd9f983c153a1cc39e6e80499978fe6bd19fe67a7b9b4b6 · schema claims-v0.9 · position 3 in the model’s answer