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Design of BQN-based decision support system and HSCNN-GPOR futuristic prediction for precision agriculture using IoT

International Journal on Smart Sensing and Intelligent Systems
Volume 18 (2025): Issue 1 (January 2025)
By:
Open Access
|Oct 2025

Figures & Tables

Figure 1:

Architecture diagram for the proposed design of BQN-based decisive supporting system and Hopfield Symmetric CNN-GPOR. BQN, Bayesian Q-Network.
Architecture diagram for the proposed design of BQN-based decisive supporting system and Hopfield Symmetric CNN-GPOR. BQN, Bayesian Q-Network.

Figure 2:

Process flow for decisive logistic associative rule-based BQN. BQN, Bayesian Q-Network.
Process flow for decisive logistic associative rule-based BQN. BQN, Bayesian Q-Network.

Figure 3:

Process flow for Hopfield Symmetric CNN-GPOR.
Process flow for Hopfield Symmetric CNN-GPOR.

Figure 4:

Hopfield Symmetric CNN architecture.
Hopfield Symmetric CNN architecture.

Figure 5:

Flowchart for the proposed design BQN-based decisive logistic transformation and Hopfield symmetric CNN-GPOR. BQN, Bayesian Q-Network.
Flowchart for the proposed design BQN-based decisive logistic transformation and Hopfield symmetric CNN-GPOR. BQN, Bayesian Q-Network.

Figure 6:

Performance of MAE in the proposed system. MAE, mean absolute error.
Performance of MAE in the proposed system. MAE, mean absolute error.

Figure 7:

Performance of RMSE in the proposed system. RMSE, root mean square error.
Performance of RMSE in the proposed system. RMSE, root mean square error.

Figure 8:

Performance of accuracy in the proposed system.
Performance of accuracy in the proposed system.

Figure 9:

Performance of loss in the proposed system.
Performance of loss in the proposed system.

Figure 10:

Performance of detection time in the proposed system.
Performance of detection time in the proposed system.

Figure 11:

Performance of detection rate in the proposed system.
Performance of detection rate in the proposed system.

Figure 12:

Performance R2 in the proposed model.
Performance R2 in the proposed model.

Figure 13:

Performance of precision in the proposed model.
Performance of precision in the proposed model.

Figure 14:

Performance of recall in the proposed model.
Performance of recall in the proposed model.

Figure 15:

Comparison of MAE. MAE, mean absolute error.
Comparison of MAE. MAE, mean absolute error.

Figure 16:

Comparison of RMSE. RMSE, root mean square error.
Comparison of RMSE. RMSE, root mean square error.

Figure 17:

Comparison of accuracy.
Comparison of accuracy.

Figure 18:

Comparison of loss.
Comparison of loss.

Figure 19:

Comparison of detection time.
Comparison of detection time.

Figure 20:

Comparison of detection rate.
Comparison of detection rate.
DOI: https://doi.org/10.2478/ijssis-2025-0050 | Journal eISSN: 1178-5608
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Language: English
Submitted on: Oct 9, 2024
Published on: Oct 4, 2025
Published by: Professor Subhas Chandra Mukhopadhyay
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
fertile soil,
water irrigation,
fuzzy logic associative rule,
Bayesian Q network,
crop health monitoring
Related subjects:
Engineering,
Introductions and overviews,
Engineering, other

© 2025 Sneha M. Khupse, Prabhakar L. Ramteke, published by Professor Subhas Chandra Mukhopadhyay
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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