Mathematical model based on Machine Learning Enabled IoT Sensing and Decision-Making in Farming
DOI:
https://doi.org/10.56294/dm20261345Keywords:
Agriculture 4.0, Attention Mechanism, Crop Disease Detection, Data Fusion, Edge Computing, Smart farmingAbstract
Introduction: The rapid expansion of global food demand, combined with unpredictable climate variability and resource scarcity, necessitates intelligent solutions for sustainable agriculture. Method: This study introduces an IoT-driven intelligent greenhouse monitoring and decision-making framework that integrates advanced machine learning (ML) models with heterogeneous environmental data. Using multi-source sensor networks and edge-cloud collaboration, the framework dynamically regulates greenhouse environments while providing yield forecasting and disease detection capabilities. Results: Experimental results demonstrate that the proposed system achieves high detection accuracy (F1 = 96.8%), low yield prediction error (RMSE = 0.40 tons/ha), and efficient energy usage (0.46 J per inference). Reinforcement learning controllers further optimize climate regulation, reducing temperature RMSE to 0.72 °C and achieving energy savings of up to 20% compared to traditional PID systems. The hybrid CNN-Transformer disease detection model outperforms benchmarks, attaining 97.9% accuracy with improved calibration reliability. Conclusion: Collectively, these findings confirm that the proposed IoT–ML framework not only improves productivity and sustainability but also ensures scalability for large-scale deployments in diverse agricultural environments.
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