Abstract: Abstract: A large amount of data has been produced with the rapid development of internet technology. The commonly-used centralized processing has posed rigorous challenges to real-time performance, low energy consumption, and data security. Alternatively, edge computing combined with Artificial Intelligence (AI) can be used to reduce the cost and energy consumption for real-time detection of complex data processing in various industries. Nowadays, agricultural applications combined with deep learning have been widely reported, such as crop disease detection, growth monitoring, yield prediction, and automated management. Edge computing can also be expected to provide more efficient solutions with the rapid development of smart agriculture. In this review, the history, concept, and architecture of edge computing were firstly introduced to evaluate the performance in intelligent agriculture. Specifically, the statistical analysis of the literature volume was carried out until May 2022, including the most reported disease identification and environmental monitoring. Secondly, the main devices of edge computing were summarized, including the Raspberry Pi, FPGA devices, NVIDIA Jetson, and smartphones. The performances of edge computing devices were also compared under different scenarios. Besides, the commonly-used deep learning was selected to promote efficiency and accuracy using the Raspberry pie 4B. Some model acceleration methods were also introduced, including network pruning, knowledge distillation, parameter quantification, and structure optimization. Then, the AI agricultural equipment with edge computing was divided into unmanned aerial vehicle (UAV), ground robots, and portable devices. Three scenarios were considered in the agriculture application, such as environmental monitoring and pest identification, crop growth and yield prediction, and variable operation of intelligent agricultural equipment. Finally, the prospects and key issues were proposed for the edge computing applied in agriculture. Several suggestions were also drawn during this time. Specifically, the edge computing application should be developed with high efficiency and accuracy. The model compression and acceleration can be the key research direction in the model deployment of deep learning. Edge computing devices can greatly contribute to smart agriculture. The cost-saving AI agricultural equipment with edge computing can also be expected to develop for much more application scenarios. The communication protocols and standards between edge devices should be established to realize the cooperative operation of multiple machines. In conclusion, edge computing was still in the initial and rapid development stage in smart agriculture. Edge computing can also provide vital opportunities and challenges for the development of smart agriculture, due to the better real-time, lower cost, and energy consumption, compared with the current cloud computing.