Claim · model-written summary
Forecast accuracy was higher in tomato plots, associated with stable irrigation and protection.
- Taxon
- tomatoes
Solanum lycopersicume0 - Treatment
- stable irrigation conditions and protective measurese0
- Outcome
- forecast accuracye0
- Direction
- highere0
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleComparatorSettingValueQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“In plots with tomatoes and sunflowers, forecast accuracy was higher, which was associated with stable irrigation conditions and protective measures.”
Title
Parameter Selection for a Neural Network Model for Forecasting Plant Pest Population Growth
Akerke Akanova; Nazira Ospanova; Galiya Anarbekova Statistics Optimization & Information Computing 2026. doi:10.19139/soic-2310-5070-3317
Other claims from this paper (13)
- Method / resource Helicoverpa armigera forecasts attained the highest R², above 0.82.
- Method / resource Frankliniella occidentalis forecasts attained the highest R², above 0.82.
- Method / resource Loxostege sticticalis forecasts performed poorest, at R² approximately 0.64.
- Association Forecast accuracy was higher in sunflower plots, associated with stable irrigation and protection.
- Method / resource Average forecast RMSE was 4.36.
- Method / resource Average forecast MAE was 3.52.
- Method / resource The optimal two-layer 64/32-neuron ReLU ADAM model yielded MAE 2.9.
- Method / resource The optimal model yielded R² 0.83.
- Method / resource Deeper models overfit.
- Method / resource Simplified models had insufficient accuracy.
- Method / resource Configurations differed significantly, p below 0.001.
- Method / resource Validation demonstrated high robustness with minimal accuracy loss across regions and seasons.
- Recommendation The approach has high practical value for agricultural pest monitoring and forecasting integration.