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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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Related Experiment Video

Updated: Jan 18, 2026

High-Throughput, In-Field Screening of Photosynthetic Efficiency in Crop Plants Using an Autonomous Robot
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Edge Computing-Enabled Smart Agriculture: Technical Architectures, Practical Evolution, and Bottleneck Breakthroughs.

Ran Gong1, Hongyang Zhang1, Gang Li1

  • 1School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China.

Sensors (Basel, Switzerland)
|September 13, 2025
PubMed
Summary

Edge computing enhances smart agriculture by processing data near the source, overcoming cloud limitations like latency and bandwidth issues for real-time decision-making in farming.

Keywords:
agricultural equipmentagricultural vehiclesedge computingreal-time monitoringsmart agriculture

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Area of Science:

  • Agricultural Technology
  • Computer Science
  • Data Science

Background:

  • Digital transformation in agriculture generates vast data, overwhelming traditional cloud computing.
  • Cloud frameworks face challenges including latency, low-power demands, and real-time constraints, impacting efficiency and security.

Purpose of the Study:

  • To provide a comprehensive survey of agricultural edge computing research.
  • To analyze the evolution of edge sensing devices, intelligent algorithms, and communication mechanisms.
  • To demonstrate the efficacy of edge computing in precision farming and crop traceability.

Main Methods:

  • Defining core concepts of edge computing in agriculture.
  • Analyzing the "terminal sensing-edge intelligence-cloud coordination" architecture.
  • Reviewing technological advancements in edge devices, algorithms, and communication.

Main Results:

  • Edge computing offers low-latency responses, bandwidth optimization, and distributed computation for agriculture.
  • Edge computing enhances real-time decision-making in precision farming, machinery control, and crop traceability.
  • Identified adaptation challenges in complex agricultural environments.

Conclusions:

  • Edge computing is a promising solution for smart agriculture, addressing cloud computing limitations.
  • Further research is needed for complex environment adaptation and future development in agricultural edge computing.