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The emergence of wireless sensor networks (WSNs) has catalyzed a paradigm shift in agricultural research and practice. In recent years, WSNs are transforming agriculture through real-time monitoring and precision resource management. This study investigates advanced models, including a hybrid WSN model integrating both terrestrial and underground components for improved data collection and energy efficiency. Techniques such as energy-aware swarm intelligence for node selection and optimized data routing paths were employed to extend network lifespan. Results showed a peak accuracy of 93.6% in the first week, with a reliability score of 0.93 for networks using 10 of these nodes. The proposed model effectively manages irrigation, monitors soil moisture, and adjusts environmental parameters to enhance crop growth. Compared to systems like LoRa Agro and MeteoHelix IoT Pro, the model demonstrated superior accuracy and efficiency, ensuring optimal crop health and resource use. The paper concludes by highlighting future trends that showcase the transformative integration of WSNs into agriculture offering a cutting-edge solution for sustainable farming, improved crop yields, water conservation, and environmental management for a greener future.

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Leveraging Energy Constrained Smart Networks for Soil Data Insights and Harvest Optimization

  • 会议论文

  • 发表日期：

    2026年

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  • Afreen Mohasin,
  • Sushruta Mishra,
  • Tiansheng Yang,
  • Lu Wang,
  • Bharati Rathore

摘要

The emergence of wireless sensor networks (WSNs) has catalyzed a paradigm shift in agricultural research and practice. In recent years, WSNs are transforming agriculture through real-time monitoring and precision resource management. This study investigates advanced models, including a hybrid WSN model integrating both terrestrial and underground components for improved data collection and energy efficiency. Techniques such as energy-aware swarm intelligence for node selection and optimized data routing paths were employed to extend network lifespan. Results showed a peak accuracy of 93.6% in the first week, with a reliability score of 0.93 for networks using 10 of these nodes. The proposed model effectively manages irrigation, monitors soil moisture, and adjusts environmental parameters to enhance crop growth. Compared to systems like LoRa Agro and MeteoHelix IoT Pro, the model demonstrated superior accuracy and efficiency, ensuring optimal crop health and resource use. The paper concludes by highlighting future trends that showcase the transformative integration of WSNs into agriculture offering a cutting-edge solution for sustainable farming, improved crop yields, water conservation, and environmental management for a greener future.