Search alternatives:
linear decrease » linear increase (Expand Search)
lower decrease » larger decrease (Expand Search), we decrease (Expand Search), showed decreased (Expand Search)
teer decrease » mean decrease (Expand Search), greater decrease (Expand Search)
linear decrease » linear increase (Expand Search)
lower decrease » larger decrease (Expand Search), we decrease (Expand Search), showed decreased (Expand Search)
teer decrease » mean decrease (Expand Search), greater decrease (Expand Search)
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2281
Evaluation metrics of selected ML models.
Published 2025“…Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”
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2282
Block diagram of the proposed system.
Published 2025“…Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”
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2283
Chart for applicable amount of fertilizers.
Published 2025“…Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”
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2284
Cost analysis of irrigation controller unit.
Published 2025“…Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”
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2285
Run times of two algorithms.
Published 2025“…Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”
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2286
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2287
Flow chart of Fuzzy Logic based control system.
Published 2025“…Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”
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2288
Block diagram for IoT-based irrigation system.
Published 2025“…Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”
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2289
Flow chart of Average Value-based control system.
Published 2025“…Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”
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2290
Hardware design for IoT-based irrigation system.
Published 2025“…Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”
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2291
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2292
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2293
Example of sample data.
Published 2025“…In contrast, the EGA-BPNN model achieves a significantly lower mean absolute relative error of 0.41% for single-flow prediction, demonstrating superior prediction performance. …”
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2294
Structure of BPNN.
Published 2025“…In contrast, the EGA-BPNN model achieves a significantly lower mean absolute relative error of 0.41% for single-flow prediction, demonstrating superior prediction performance. …”
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2295
The workflow of EGA-BPNN.
Published 2025“…In contrast, the EGA-BPNN model achieves a significantly lower mean absolute relative error of 0.41% for single-flow prediction, demonstrating superior prediction performance. …”
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2296
S1 Data -
Published 2025“…In contrast, the EGA-BPNN model achieves a significantly lower mean absolute relative error of 0.41% for single-flow prediction, demonstrating superior prediction performance. …”
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2297
Algorithm flow of the GA-BPNN model.
Published 2025“…In contrast, the EGA-BPNN model achieves a significantly lower mean absolute relative error of 0.41% for single-flow prediction, demonstrating superior prediction performance. …”
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2298
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2299
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2300