AIoT-Enabled Data Management for Smart Agriculture: A Comprehensive Review on Emerging Technologies - Xu Luo; Shuming Xiong; Xiangchen Jia; Yu Zeng; Xiao Chen (IEEE Access)
The deep integration of artificial intelligence (AI) and Internet of Things (IoT) technologies has promoted the application of Artificial Intelligence of Things (AIoT) in smart agriculture, driving substantial transformations in agricultural data management systems and facilitating sustainable agricultural development. This paper reviews the key AIoT data management technologies employed in smart agriculture, systematically exploring the technical applications and practical implementations across four architectural layers. These layers (i.e., perception layer, transport layer, data platform layer, and application layer) correspond to agricultural information sensing, data transmission, storage processing, and intelligent decision-making, respectively. Research indicates that, through the deployment of diverse sensing devices, low-latency short-range communications, wide-area wireless networks, cloud-edge collaborative computing, blockchain-based data storage, and AI-driven agricultural decision support systems, the data management framework of smart agriculture is undergoing continuous and intelligent enhancement. AIoT exhibits significant potential in enhancing agricultural productivity, optimizing resource allocation, and improving environmental adaptability. However, significant challenges remain, particularly in protecting agricultural data privacy and achieving real-time interoperability across heterogeneous platforms. The paper concludes by outlining future research directions, offering valuable insights for researchers in the field of smart agriculture.