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April 10, 2026InformationOpen Access

FPGA-Accelerated Machine Learning for Computational Environmental Information Processing in IoT-Integrated High-Density Nanosensor Networks

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Authors

ADAlaa Kamal Yousif DafhallaFAFayaz AliImam Abdulrahman Bin Faisal UniversityAEAsma Ibrahim Gamar EldeenImam Abdulrahman Bin Faisal University

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Implication

Demonstrates enhanced environmental control in precision horticulture using FPGA and IoT, indicating promising advancements.

Key Points

  • The aim is to optimize microclimate conditions in precision horticulture using FPGA-accelerated machine learning.
  • Developed a nanosensor network system for environmental regulation.
  • Employed a random forest regression model embedded in FPGA for predictive control.
  • Utilized an IoT gateway for real-time monitoring and supervision.
  • Achieved an R2 of 0.985 and RMSE of 0.28 °C for predictive accuracy.
  • Maintained environmental parameters within ±0.3 °C and ±1.2% humidity of setpoints.
  • Increased fresh biomass by 28% and reduced overall energy consumption by 18% compared to traditional methods.

Cite This Study

Dafhalla et al. (2026) studied this question.

synapsesocial.com/papers/69d8970c6c1944d70ce084a4https://doi.org/10.3390/info17040354
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