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<p>Real-time sensors for precision irrigation schedulating are used for enhancing water efficiency and optimizing resource usage. Poor resource management can negatively impact traditional farming practices, particularly in regions limited by water shortages. Agriculture is susceptible due to its heavy reliance on water resources. Due to global warming and its potential impacts, there is a growing emphasis on developing strategies to ensure a steady water supply for food production and consumption. As a result, research on reducing water usage in irrigation systems needs to be implemented. While traditional commercial irrigation sensors are often too expensive for smaller farms to adopt, manufacturers are now producing affordable alternatives that can be integrated with network systems to provide cost-effective solutions for efficient irrigation and agricultural monitoring. To minimize a farmer’s efforts, an Internet of Things (IoT)-based drip irrigation system is proposed in this work. Initially, the required data is collected using the IoT sensors. The gathered data is fed into the Adaptive Residual Hybrid network (ARHN) that is developed by using the Spatial Autoencoder and Stacked CapsNet. Here, the Modernized Random Variable-based Frilled Lizard Optimization (MRV-FLO) is utilized to tune the ARHN parameters. Therefore, the required water from the pump for the crops is provided by the ARHN model. In addition, this model makes the work simpler and avoids the wastage of water in the agricultural environment. Finally, the performance of the developed framework is validated over the existing works to prove the efficiency of the recommended method. The main experimental findings of the developed model achieve 99.24% and 97.32% in terms of accuracy and RMSE. Moreover, the statistical findings of the developed model shows 41.9%, 34.9%, 36.0% and 37.1% better performance than LEA-ARHN, FDA-ARHN, AOA-ARHN and FLO-ARHN in terms of best measure. Based on this performance enhancement, the developed model can effectively reduces the farmer’s effort and improves the crop productivity in the agricultural sectors.</p>

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Automated smart drip irrigation system in internet of things using adaptive residual hybrid network for precision farming

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月30日

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  • Ahmad Y. A. Bani Ahmad,
  • Jafar A. Alzubi,
  • Chanthirasekaran K.,
  • Shabana Urooj,
  • Mohammad Shahzad,
  • Yogapriya J.

摘要

Real-time sensors for precision irrigation schedulating are used for enhancing water efficiency and optimizing resource usage. Poor resource management can negatively impact traditional farming practices, particularly in regions limited by water shortages. Agriculture is susceptible due to its heavy reliance on water resources. Due to global warming and its potential impacts, there is a growing emphasis on developing strategies to ensure a steady water supply for food production and consumption. As a result, research on reducing water usage in irrigation systems needs to be implemented. While traditional commercial irrigation sensors are often too expensive for smaller farms to adopt, manufacturers are now producing affordable alternatives that can be integrated with network systems to provide cost-effective solutions for efficient irrigation and agricultural monitoring. To minimize a farmer’s efforts, an Internet of Things (IoT)-based drip irrigation system is proposed in this work. Initially, the required data is collected using the IoT sensors. The gathered data is fed into the Adaptive Residual Hybrid network (ARHN) that is developed by using the Spatial Autoencoder and Stacked CapsNet. Here, the Modernized Random Variable-based Frilled Lizard Optimization (MRV-FLO) is utilized to tune the ARHN parameters. Therefore, the required water from the pump for the crops is provided by the ARHN model. In addition, this model makes the work simpler and avoids the wastage of water in the agricultural environment. Finally, the performance of the developed framework is validated over the existing works to prove the efficiency of the recommended method. The main experimental findings of the developed model achieve 99.24% and 97.32% in terms of accuracy and RMSE. Moreover, the statistical findings of the developed model shows 41.9%, 34.9%, 36.0% and 37.1% better performance than LEA-ARHN, FDA-ARHN, AOA-ARHN and FLO-ARHN in terms of best measure. Based on this performance enhancement, the developed model can effectively reduces the farmer’s effort and improves the crop productivity in the agricultural sectors.