Edge Computing-Enabled Smart Agriculture: Technical Architectures, Practical Evolution, and Bottleneck Breakthroughs

As the global digital transformation of agriculture accelerates, the widespread deployment of farming equipment has triggered an exponential surge in agricultural production data. Consequently, traditional cloud computing frameworks face critical challenges: communication latency in the field, the d...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 25; no. 17; p. 5302
Main Authors: Gong, Ran, Zhang, Hongyang, Li, Gang, He, Jiamin
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 26.08.2025
Subjects:
Agricultural cooperatives
agricultural equipment
Agricultural industry
Agricultural land
Agricultural production
agricultural vehicles
Agriculture
Algorithms
Bandwidths
Bottlenecks
Climate change
Communication
Data compression
Data processing
Data transmission
Decision making
Edge computing
Efficiency
Farm machinery
Optimization
Physiology
Privacy
real-time monitoring
Sensors
smart agriculture
agricultural vehicles
real-time monitoring
smart agriculture
edge computing
agricultural equipment
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:As the global digital transformation of agriculture accelerates, the widespread deployment of farming equipment has triggered an exponential surge in agricultural production data. Consequently, traditional cloud computing frameworks face critical challenges: communication latency in the field, the demand for low-power devices, and stringent real-time decision constraints. These bottlenecks collectively exacerbate bandwidth constraints, diminish response efficiency, and introduce data security vulnerabilities. In this context, edge computing offers a promising solution for smart agriculture. By provisioning computing resources to the network periphery and enabling localized processing at data sources adjacent to agricultural machinery, sensors, and crops, edge computing leverages low-latency responses, bandwidth optimization, and distributed computation capabilities. This paper provides a comprehensive survey of the research landscape in agricultural edge computing. We begin by defining its core concepts and highlighting its advantages over cloud computing. Subsequently, anchored in the “terminal sensing-edge intelligence-cloud coordination” architecture, we analyze technological evolution in edge sensing devices, lightweight intelligent algorithms, and cooperative communication mechanisms. Additionally, through precision farming, intelligent agricultural machinery control, and full-chain crop traceability, we demonstrate its efficacy in enhancing real-time agricultural decision-making. Finally, we identify adaptation challenges in complex environments and outline future directions for research and development in this field.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s25175302