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<p>Soil properties play a pivotal role in determining agricultural productivity. This paper presents a cost-efficient, rule-based IoT architecture for smart agriculture, focusing on optimizing soil–water evaporation kinetics. Four IoT architectures—two Edge-Cloud-based and two Edge-Fog-Cloud-based—were implemented and analyzed. The integration of real-time sensing and actuation using ESP8266, ESP32, and Raspberry Pi enabled precise irrigation control. A novel rule-based system incorporating the Pruned Exact Linear Time (PELT) algorithm was proposed to minimize data transmission, storage, and processing costs. The experimental analysis demonstrated significant improvements in scalability, battery efficiency, and cloud resource utilization. By tailoring irrigation schedules based on the detected change points, the system achieves enhanced water and energy conservation while maintaining soil health. A comparative cost evaluation across different cloud storage tiers validates the financial viability of the system. This approach provides a scalable and sustainable framework for precision agriculture in resource-constrained environments. The results demonstrate that the proposed rule-based algorithms can achieve substantial reductions in cloud storage costs while maintaining computational feasibility for real-world deployment in smart agricultural systems.</p>

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Cost Efficient Rule Based Soil–Water Kinetics Driven Automated Architecture for Smart Agriculture

  • 期刊论文

  • 发表日期：

    2025年7月17日

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  • Dola Gupta,
  • Anjan Kumar Dasgupta,
  • Kaustav Chakraborty,
  • Amlan Chakrabarti

摘要

Soil properties play a pivotal role in determining agricultural productivity. This paper presents a cost-efficient, rule-based IoT architecture for smart agriculture, focusing on optimizing soil–water evaporation kinetics. Four IoT architectures—two Edge-Cloud-based and two Edge-Fog-Cloud-based—were implemented and analyzed. The integration of real-time sensing and actuation using ESP8266, ESP32, and Raspberry Pi enabled precise irrigation control. A novel rule-based system incorporating the Pruned Exact Linear Time (PELT) algorithm was proposed to minimize data transmission, storage, and processing costs. The experimental analysis demonstrated significant improvements in scalability, battery efficiency, and cloud resource utilization. By tailoring irrigation schedules based on the detected change points, the system achieves enhanced water and energy conservation while maintaining soil health. A comparative cost evaluation across different cloud storage tiers validates the financial viability of the system. This approach provides a scalable and sustainable framework for precision agriculture in resource-constrained environments. The results demonstrate that the proposed rule-based algorithms can achieve substantial reductions in cloud storage costs while maintaining computational feasibility for real-world deployment in smart agricultural systems.