Jiangtao Deng1,2, Shuya Zhao3, Jijing Cai3, Yuchao Xia3, Meilei Lv2This email address is being protected from spambots. You need JavaScript enabled to view it., Kai Fang3This email address is being protected from spambots. You need JavaScript enabled to view it., Hailin Feng3, and Thippa Reddy Gadekallu3,4
1College of Automation, Hangzhou Dianzi University, Hangzhou, China
2College of Electrical and Information Engineering, Quzhou University, Quzhou, China
3College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou, China
4Division of Research and Development, Lovely Professional University, Phagwara, India
Received: December 23, 2025 Accepted: January 26, 2026 Publication Date: February 26, 2026
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
Consumer-grade Agricultural AIoT (Agri-AIoT) systems increasingly rely on cloud-based intelligence, which introduces latency, privacy, and connectivity limitations. These limitations are particularly severe for heterogeneous consumer devices operating under strict cost, energy, and computational constraints. This review advocates a shift from cloud-centric architectures toward distributed, edge-centric intelligence. We examine lightweight model compression techniques, including pruning, quantization, and knowledge distillation, that enable real-time and on-device decision-making. We further analyze security threats such as data poisoning and adversarial attacks that arise in decentralized agricultural systems. Privacy-preserving learning mechanisms, including Federated Learning, are discussed as key enablers of collaborative intelligence without raw data sharing. By integrating lightweight Artificial Intelligence techniques with AI-native networking principles, this paper provides a unified perspective on distributed intelligence in consumer Agri-AIoT ecosystems. We conclude that the convergence of these approaches is essential for building sustainable, secure, and self-adaptive consumer-grade agricultural electronics.
Keywords: AI-Native Networking; Resource-Constrained Devices; Agri-AIoT; Lightweight AI
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