Abstract:

[Significance] Against the backdrop of consumption upgrading and growing health awareness, consumer concerns about agricultural products are shifting from mere safety to freshness, taste, and brand reputation, making quality a core target of modern agricultural upgrading and accelerating the transition of production modes toward precise and intelligent control. However, the traditional centralized cloud‑computing architecture dominated by a central cloud platform is increasingly constrained in weak‑network environments, low‑latency control, and local autonomy, and thus can hardly support the closed‑loop requirements of "timely detection–rapid response–continuous optimization" in quality control. Consequently, cloud–edge–device integrated collaborative architectures are regarded as an important development direction, and it is necessary to systematically sort out their key technologies and application foundations in the agricultural domain so as to provide unified theoretical and technical support for the design of integrated middleware oriented to agricultural product quality control. [Progress] Starting from the current application status of cloud–edge–device integration in agriculture, the paper focuses on three typical scenarios—field planting, facility horticulture, and livestock farming—and summarizes the functional division of perception, computation, and control among cloud, edge, and device, as well as the differences in business requirements under various geographical conditions and production modes. On this basis, it identifies multiple technical challenges in practical deployments, including heterogeneous adaptation difficulties caused by the coexistence of numerous industrial and IoT protocols, poor interoperability due to inconsistent data definitions and encodings, resource imbalance arising from the mismatch between edge computing capacity and task loads, and coarse‑grained, inefficient cloud–edge collaborative scheduling. To address these challenges, the paper synthesizes relevant research on artificial intelligence, edge computing, federated learning, and blockchain, and proposes improvement ideas for cloud–edge–device collaboration from the perspectives of resource‑allocation optimization, edge‑intelligent inference, privacy protection, and trusted sharing. Furthermore, targeting the concrete requirements of intelligent quality control for agricultural products, it constructs a cloud–edge–device integrated technical framework: at the data layer, it integrates standardized mapping of quality data and a multi‑source data dictionary system; at the access layer, it realizes heterogeneous device‑protocol conversion and unified access; at the computing layer, it achieves dynamic matching between computing resources and services through intelligent task scheduling and load balancing, multi‑level caching, model hot‑updating, and federated deployment. At the collaboration level, it proposes a "device–crop–model" trinity abstraction that brings physical devices, agronomic objects, and algorithmic models under unified middleware management, thereby masking underlying hardware differences while supporting elastic adaptation and capability reuse across diverse scenarios. [Conclusions and Prospects] The cloud–edge–device integrated collaboration and its adaptive middleware constitute the key infrastructure and connective hub of intelligent quality‑control systems for agricultural products, providing reusable architectural concepts and technical references for the development and engineering implementation of systems for precise quality perception, automatic control, and intelligent decision‑making. Looking ahead, further in‑depth research is needed on cross‑scenario data and protocol standard systems oriented to quality indicators and control actions, on the integrated application of edge intelligence and large models, on the enhancement of privacy‑protection and trusted‑computing mechanisms, and on large‑scale engineering demonstrations and validation, so as to continuously improve the intelligence level, operational reliability, and scalability of agricultural product quality‑control systems and to promote the transition of related technologies from pilot applications to large‑scale deployment.

Key words: internet of things, cloud-edge-device integration, middleware, agricultural product quality, intelligent control