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In smart farming, IoT-related technologies are gaining popularity. The ultimate goal is to collect, monitor, and use important data for agricultural operations to optimize and sustain agriculture. Smart networks have advanced thanks to the Internet of Things. This research provides a LoRa-IoT-based efficient data prediction model for agricultural applications utilizing a Deep Maxout Neural Network (DMNN) and IoT Agriculture Dataset. LoRa is a leading Internet of Things technology because it can transmit vast distances with low power. A customized smart farming system using IoT and LoRa technologies and a low-cost, low-power, wide-range wireless sensor network is presented in this research. Data is analyzed using the DMNN architecture to estimate crop yields, irrigation needs, and disease outbreaks. Extensive trials show that the suggested approach outperforms existing predictive models in data processing and prediction. This model has 99.23% accuracy, 25.53% RMSE, 13.52% MAE, 9.62%, MAPE, 0.9905 R-square, and 1.49 s processing time.

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LoRa-IoT-Based Efficient Data Prediction Model for Agricultural Applications Using Deep Maxout Neural Network

  • 会议论文

  • 发表日期：

    2025年

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  • N. Dharmaraj,
  • D. Srimathy,
  • N. Divyesh,
  • E. Maharajaveni,
  • A. M. Praveen Karthik,
  • C. Surya

摘要

In smart farming, IoT-related technologies are gaining popularity. The ultimate goal is to collect, monitor, and use important data for agricultural operations to optimize and sustain agriculture. Smart networks have advanced thanks to the Internet of Things. This research provides a LoRa-IoT-based efficient data prediction model for agricultural applications utilizing a Deep Maxout Neural Network (DMNN) and IoT Agriculture Dataset. LoRa is a leading Internet of Things technology because it can transmit vast distances with low power. A customized smart farming system using IoT and LoRa technologies and a low-cost, low-power, wide-range wireless sensor network is presented in this research. Data is analyzed using the DMNN architecture to estimate crop yields, irrigation needs, and disease outbreaks. Extensive trials show that the suggested approach outperforms existing predictive models in data processing and prediction. This model has 99.23% accuracy, 25.53% RMSE, 13.52% MAE, 9.62%, MAPE, 0.9905 R-square, and 1.49 s processing time.