Claim · model-written summary
GNNs predicted potato yield with an MSE of 0.02363.
- Taxon
- Potato
Solanum tuberosumt0 - Outcome
- MSEe0
- Value
- 0.02363e0
- Qualifier
- GNNse0
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“Potato Crop Yield”
e0“GNNs displayed an MSE of 0.02363 and an R 2 of 0.51719, excelling in the overall performance”
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 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.
- Method / resource GNNs and LSTMs captured complex spatial and temporal patterns in the potato yield data.