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The Internet of Things (IoT) allows equipment to interact and run without human control, offering enormous opportunities for advancements in agriculture. Smart farming using IoT monitors key variables including soil type, moisture, nutrients, temperature, and light. Although development has been achieved, IoT integration in agriculture poses challenges to maximise data analysis for higher agricultural output. This effort tracks significant agricultural parameters by sending data using many sensors to the Thing Speak IoT cloud platform. Data analysis generates Deep Belief Network (DBN) practical insights for farmers. With 95% accuracy in soil moisture detection, 90% accuracy in temperature monitoring, and an 85% gain in decision-making efficiency the proposed system provides enhanced crop output estimates.

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Improved Data Analytics Using Deep Learning Algorithm in Internet of Things for Crop Yield Prediction

  • 会议论文

  • 发表日期：

    2026年

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  • K. Sabitha,
  • M. Rajeswari,
  • D. Lita Pansy

摘要

The Internet of Things (IoT) allows equipment to interact and run without human control, offering enormous opportunities for advancements in agriculture. Smart farming using IoT monitors key variables including soil type, moisture, nutrients, temperature, and light. Although development has been achieved, IoT integration in agriculture poses challenges to maximise data analysis for higher agricultural output. This effort tracks significant agricultural parameters by sending data using many sensors to the Thing Speak IoT cloud platform. Data analysis generates Deep Belief Network (DBN) practical insights for farmers. With 95% accuracy in soil moisture detection, 90% accuracy in temperature monitoring, and an 85% gain in decision-making efficiency the proposed system provides enhanced crop output estimates.