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<p>Mobile wireless sensor networks (WSNs) have made significant progress in various applications. However, much of the existing research has focused on 2D planar domains, despite the substantial need for three-dimensional (3D) monitoring in many real-world scenarios. Agriculture is one of the most promising fields that requires 3D monitoring. This makes 3D mobile distributed WSNs essential for effective environmental management. In this context, the present paper explores their application in agricultural settings. Specifically, the study analyzes energy consumption patterns within these 3D networks and explores innovative energy harvesting techniques to ensure the sustainability of sensor networks in farming environments. Moreover, this research integrates machine learning with 3D mobile sensors within distributed WSNs to enhance smart agriculture practices. The study focuses on optimizing irrigation processes through precision watering and improving pesticide usage with data-driven decision-making. Consequently, this promotes more sustainable and efficient agricultural practices. The obtained results highlight the significant impact of machine learning-powered 3D mobile distributed WSNs in driving progress in smart agriculture, especially in optimizing resources and promoting environmental sustainability.</p>

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Integrating machine learning for smart agriculture: 3D mobile distributed wireless sensors network and object recognition

  • 期刊论文

  • 发表日期：

    2025年5月25日

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  • Nadia Boufares,
  • Fatma Sbiaa,
  • Yosra Ben Saied,
  • Leila Azouz Saidane

摘要

Mobile wireless sensor networks (WSNs) have made significant progress in various applications. However, much of the existing research has focused on 2D planar domains, despite the substantial need for three-dimensional (3D) monitoring in many real-world scenarios. Agriculture is one of the most promising fields that requires 3D monitoring. This makes 3D mobile distributed WSNs essential for effective environmental management. In this context, the present paper explores their application in agricultural settings. Specifically, the study analyzes energy consumption patterns within these 3D networks and explores innovative energy harvesting techniques to ensure the sustainability of sensor networks in farming environments. Moreover, this research integrates machine learning with 3D mobile sensors within distributed WSNs to enhance smart agriculture practices. The study focuses on optimizing irrigation processes through precision watering and improving pesticide usage with data-driven decision-making. Consequently, this promotes more sustainable and efficient agricultural practices. The obtained results highlight the significant impact of machine learning-powered 3D mobile distributed WSNs in driving progress in smart agriculture, especially in optimizing resources and promoting environmental sustainability.