This review critically examines advancements in sensing, control, and networking technologies for agricultural robots (AgRobots) and their impact on modern farming. AgRobots—including Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs), Unmanned Surface Vehicles (USVs), and robotic arms—are increasingly adopted to address labour shortages, sustainability challenges, and rising food demand. This paper reviews sensing technologies such as cameras, LiDAR, and multispectral sensors for navigation, object detection, and environmental perception. Control approaches, from classical PID (Proportional-Integral-Derivative) to advanced nonlinear and learning-based methods, are analysed to ensure precision, adaptability, and stability in dynamic agricultural settings. Networking solutions, including ZigBee, LoRaWAN, 5G, and emerging 6G, are evaluated for enabling real-time communication, multi-robot coordination, and data management. Swarm robotics and hybrid decentralized architectures are highlighted for efficient collective operations. This review is based on the literature published between 2015 and 2025 to identify key trends, challenges, and future directions in AgRobots. While AgRobots promise enhanced productivity, reduced environmental impact, and sustainable practices, barriers such as high costs, complex field conditions, and regulatory limitations remain. This review is expected to provide a foundation for guiding research and development toward innovative, integrated solutions for global food security and sustainable agriculture.

Targeted weed management is an important element of precision agriculture, and accurate weed identification is a foundation for precision weed control. Over the past decade, convolutional neural networks have demonstrated high accuracy and generalization in recognizing weeds in agricultural environments. Edge computing, via edge devices, is one secure method to effectively deploy artificial intelligence algorithms (AI) for weed control on agricultural platforms. This study presents a bibliometric and systematic review of AI algorithms and edge-based systems for precision weed control from 2015 to 2025. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a systematic search was conducted in Scopus and Web of Science, resulting in the inclusion of 43 documents. Results show a significant surge in publications on AI-based weed control systems deployed on edge computing resources since 2019. The analysis reveals that RGB cameras are the preferred data acquisition method, while object detection models, specifically the YOLO family, are widely adopted for AI deployment. Pretraining or training optimizations are preferred over post-training model optimization to maximize inference and improve detection accuracy, while NVIDIA Jetson series edge devices are commonly used for deploying AI algorithms. Precision chemical control approaches dominate, but laser weeding technology is emerging as an alternative. Despite technological advances, challenges hinder commercial deployment, including reliance on manual data annotation and model vulnerability to environmental variability. Future research should focus on semi-supervised learning, synthetic data generation, and multimodal vision systems to improve robustness and reduce annotation dependence. Unified evaluation protocols are also needed to benchmark system performance. Received: 22 December 2025 | Revised: 24 February 2026 | Accepted: 23 March 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Adeayo Adewumi: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Dharmendra Saraswat: Conceptualization, Methodology, Validation, Resources, Writing – review & editing, Supervision, Project administration, Funding acquisition.