Claim · model-written summary
GNNs and LSTMs captured complex spatial and temporal patterns in the potato yield data.
- Taxon
- Potato
Solanum tuberosumt0 - 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.

Verbatim
t0“Potato Crop Yield”
e0“GNNs and LSTMs not only have the advantage of high accuracy but also capture the complex spatial and temporal patterns in the data”
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)
- Method / resource Gradient boosting predicted potato yield with an MSE of 0.03438.
- Method / resource Gradient boosting predicted potato yield with an R 2 of 0.49168.
- Method / resource XGBoost predicted potato yield with an MSE of 0.03583.
- Method / resource XGBoost predicted potato yield with an R 2 of 0.35106.
- Method / resource GNNs predicted potato yield with an MSE of 0.02363.
- Method / resource GNNs predicted potato yield with an R 2 of 0.51719.
- Method / resource LSTMs predicted potato yield with an MSE of 0.03177.
- Method / resource GRUs predicted potato yield with an MSE of 0.03150.