Method / resourcemodel-written summary

GNNs and LSTMs captured complex spatial and temporal patterns in the potato yield data.

A claim from Predicting Potato Crop Yield with Machine Learning and Deep Learning for Sustainable Agriculture · 2024

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

GNNs and LSTMs captured complex spatial and temporal patterns in the potato yield data.

Outcome
capture the complex spatial and temporal patterns in the datae0
Value
high accuracye0

Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirectionQualifier

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

Solanum tuberosum
Solanum tuberosum · TaxonScott Bauer, USDA ARS · Public domain · Wikimedia Commons ↗

Verbatim

t0

“Potato Crop Yield”

✓ located verbatim in the source · title, characters 11–28
e0

“GNNs and LSTMs not only have the advantage of high accuracy but also capture the complex spatial and temporal patterns in the data”

✓ located verbatim in the source · abstract, characters 1196–1326

Title

Predicting Potato Crop Yield with Machine Learning and Deep Learning for Sustainable Agriculture

El-Sayed M. El-Kenawy; Amel Ali Alhussan; Nima Khodadadi; et al. Potato Research 2024. doi:10.1007/s11540-024-09753-w

Other claims from this paper (8)

Record rev-e8fb4a1aef67a698a45eee3405020ba1eebba6c552077c0edf2542a8a81ab6ce · schema claims-v0.9 · position 9 in the model’s answer