Swarm intelligence in agricultural robotics has emerged as a strategic frontier that can simultaneously boost productivity and sustainability in modern agriculture. By enabling safe, reliable, autonomous, and efficient operations across open, dynamic, and partially structured farm environments, swarm intelligence provides a unifying paradigm for transitioning from single-robot autonomy to coordinated multi-robot systems. This paper presents a systematic review of the fundamental connotation, elements, key technologies, and future directions of agricultural robot swarm intelligence. We first clarify the concept and origins of swarm intelligence and summarize its defining properties—distributed control, self-organization, and dynamic adaptability—then propose a domain-specific conceptual framework for agricultural robot swarms centered on “Individual autonomy–Information sharing–Swarm collaboration.” We then delve into the four critical technological pillars underpinning agricultural robot swarm intelligent (ARSI): collaborative perception, collaborative planning, collaborative control, and ground-air cross-domain collaboration. For collaborative perception, we discuss multi-robot localization and mapping in non-structured fields, emphasizing robustness under GNSS multipath, vegetation occlusion, and seasonal appearance changes. Representative SLAM and semantic mapping strategies are compared, including centralized and fully distributed map fusion, bandwidth-aware loop closure, and spatiotemporal synchronization across heterogeneous sensors. We highlight advances that fuse LiDAR-vision-inertial data, improve robustness in highly dynamic scenes, and maintain long-term map consistency for seasonal operations. In collaborative planning, we review task allocation and motion planning under large task scales, heterogeneous robot capabilities, and tight operation windows. Four families of task assignment methods are contrasted—exact optimization, market/auction mechanisms, bio-inspired metaheuristics, and learning-driven approaches—together with their trade-offs in optimal, scalability, and responsiveness. For coverage and path planning, we summarize graph-search, sampling-based, potential-field, and intelligent optimization methods, and discuss trajectory tracking for synchronized multi-robot execution using techniques such as artificial potential fields, spline smoothing, and model predictive control. For collaborative control, we briefly review formation and consensus methods (leader-follower, behavior-based control, event-triggered schemes, and predictive control) that ensure safety, precision, and resource efficiency in long-duration field operations. Special attention is given to region-reaching consensus for multi-task area operations, event-triggered formation control that reduces computation/communication loads while preserving tracking accuracy, and robust schemes that accommodate model uncertainties. We then analyze ground–air cross-domain collaboration, wherein UAVs provide wide-area monitoring, semantic mapping, and communication relays, while UGVs execute high-precision, energy-efficient interventions. We discuss registration and fusion between aerial and ground maps, closed-loop pipelines from perception to targeted actuation (e.g., site-specific weeding/spraying), and the scalability challenges of communication, safety, and real-time scheduling in large deployments. In addition, we present compelling case studies from global initiatives and commercial deployments, such as the RHEA project’s weed control system, SwarmFarm’s cloud-optimized fleet, and Carbon Robotics’ laser-weeding technology, demonstrating tangible benefits in reducing chemical inputs and labor costs. Finally, we outline five key future research directions: 1) the development of domain-specific agricultural foundation models to drive knowledge-informed decision-making; 2) the creation of comprehensive “Farm-Digital Twin” platforms for closed-loop simulation and re-planning; 3) the adoption of hierarchical edge-cloud architectures to manage computational and communication bottlenecks; 4) the advancement of dynamic, game-theoretic planning frameworks for highly uncertain environments; and 5) the standardization and large-scale validation of ground-air collaborative systems. This synthesis aims to provide researchers and industry stakeholders with a clear roadmap for advancing the field and accelerating the deployment of intelligent, scalable, and sustainable robotic solutions for the future of agriculture.

With the emergence of the artificial intelligence era, all kinds of robots are traditionally used in agricultural production. However, studies concerning the robot task assignment problem in the agriculture field, which is closely related to the cost and efficiency of a smart farm, are limited. Therefore, a Multi-Weeding Robot Task Assignment (MWRTA) problem is addressed in this paper to minimize the maximum completion time and residual herbicide. A mathematical model is set up, and a Multi-Objective Teaching-Learning-Based Optimization (MOTLBO) algorithm is presented to solve the problem. In the MOTLBO algorithm, a heuristic-based initialization comprising an improved Nawaz Enscore, and Ham (NEH) heuristic and maximum load-based heuristic is used to generate an initial population with a high level of quality and diversity. An effective teaching-learning-based optimization process is designed with a dynamic grouping mechanism and a redefined individual updating rule. A multi-neighborhood-based local search strategy is provided to balance the exploitation and exploration of the algorithm. Finally, a comprehensive experiment is conducted to compare the proposed algorithm with several state-of-the-art algorithms in the literature. Experimental results demonstrate the significant superiority of the proposed algorithm for solving the problem under consideration.