Gong Ran, Zhang Hongyang, Li Gang, He Jiamin
School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China.
Sensors (Basel). 2025 Aug 26;25(17):5302. doi: 10.3390/s25175302.
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.
随着全球农业数字化转型加速，农业设备的广泛部署引发了农业生产数据呈指数级增长。因此，传统云计算框架面临严峻挑战：现场通信延迟、对低功耗设备的需求以及严格的实时决策约束。这些瓶颈共同加剧了带宽限制，降低了响应效率，并带来了数据安全漏洞。在此背景下，边缘计算为智慧农业提供了一个有前景的解决方案。通过在网络边缘提供计算资源，并在与农业机械、传感器和农作物相邻的数据源处实现本地化处理，边缘计算利用了低延迟响应、带宽优化和分布式计算能力。本文全面综述了农业边缘计算的研究现状。我们首先定义其核心概念，并强调其相对于云计算的优势。随后，基于“终端感知-边缘智能-云协同”架构，我们分析了边缘传感设备、轻量级智能算法和协作通信机制的技术演进。此外，通过精准农业、智能农机控制和全链作物可追溯性，我们展示了其在增强农业实时决策方面的功效。最后，我们识别了复杂环境中的适配挑战，并概述了该领域未来的研发方向。
