Research map / Architectures and efficiency / RNNs and normalization
Embedding a Long Short-Term Memory Network in a Constraint Programming Framework for Tomato Greenhouse Optimisation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use DSS-selected greenhouse actions instead of the experienced grower's actions | Greenhouse control · Tomato greenhouse, 5/8/2021 | Profit (C/m): 0.29 -> 0.40 | Helped |
| Use DSS-selected greenhouse actions instead of the experienced grower's actions | Greenhouse control · Tomato greenhouse, 5/8/2021 | Yield (g/m): 256.89 -> 385.31 | Helped |
| Embed the LSTM climate model in the CP framework | LSTM climate prediction · Tomato greenhouse, 5/8/2021–7/8/2021 | RMSE: 0.0650 -> 0.0665 | No clear effect |
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