Claim · model-written summary
Combining with a DNN decreased the RRMSE of indoor air vapor pressure to 12.6%.
- Taxon
- tomato
Solanum lycopersicumt0 - Outcome
- RRMSE of indoor air vapor pressuree3
- Direction
- decreasede3
- Value
- 12.6%e3
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
t0“greenhouse climate-tomato production model”
e3“and decreased the RRMSE of indoor air vapor pressure to 12.6%”
Title
Xiaohan Zhou; Qingzhi Liu; David Katzin; et al. Computers and Electronics in Agriculture 2023. doi:10.1016/j.compag.2023.107980
Other claims from this paper (7)
- Method / resource PF-calibration decreased the seasonal RRMSE of indoor air vapor pressure predictions to 16.4% from 40.7% before calibration.
- Intervention effect PF-calibration did not decrease the RRMSE of indoor air temperature predictions.
- Method / resource Combining the PF-calibrated model with a DNN decreased the RRMSE of indoor air temperature to 6.7% from 15.0% without DNN.
- Method / resource The model had a relative error of 0.9% for seasonal total fresh yield.
- Method / resource The model had an RRMSE of 6.6% for cumulative yield over the season.
- Intervention effect Without calibration before combining with DNNs, the required amount and diversity of DNN training data increased.
- Recommendation The three-step knowledge-based data-driven approach is recommended to improve model prediction accuracy.