AI-Driven Cooperative Control for Autonomous Tractors and Implements: A Comprehensive Review

Abstract

Artificial intelligence (AI) is driving the evolution of autonomous agriculture towards multi-agent collaborative control, breaking through the limitations of traditional isolated automation. Although existing research has focused on hierarchical control and perception-decision-making technologies for agricultural machinery, the overall integration of these elements in building a resilient physical perception collaborative system is still insufficient. This paper systematically reviews the progress of AI-driven tractor-implement cooperative control from 2018 to 2025, focusing on four major technical pillars: (1) perception-decision-execution hierarchical architecture, (2) distributed multi-agent collaborative framework, (3) physical perception modeling and adaptive control, and (4) staged operation applications (such as collaborative harvesting). The research reveals core challenges such as real-time collaborative planning, perception robustness under environmental disturbances, and collaborative control and safety assurance under operational disturbances. To this end, three solutions are proposed: an AI framework for formalizing agronomic constraints and mechanical dynamics; a disturbance-resistant adaptive tractor-implement cooperative control strategy; and a real-time collaborative ecosystem integrating neuromorphic computing and FarmOS. Finally, a research roadmap is summarized with agronomic constraint reinforcement learning, self-reconfigurable collaboration, and biomechanical mechatronic systems as the core. By integrating the scattered progress in AI, robotics and agronomy, we provide theoretical foundation and practical guidance for scalable and sustainable autonomous farm systems.

1. Introduction

The global agricultural sector is facing unprecedented challenges driven by a confluence of factors: a growing labor shortage in rural areas, the urgent need to ensure food security for a growing population, and the need to reduce the environmental impact of agricultural practices [1,2]. These pressures are exposing the limitations of traditional and even early automated agricultural models, which suffer from low resource utilization and difficulty adapting to complex, unstructured environments [2].

To this end, autonomous agricultural machinery represents a critical advancement. Technologies such as camera-guided tractors show potential in reducing operator workload and improving the precision of basic operations [3,4]. However, these standalone automated systems still have significant limitations. Their effectiveness decreases on large farms, and their limited environmental perception prevents them from handling complex, dynamic scenarios [5,6]. Most existing research still focuses on a single technical dimension, such as optimization algorithms for path planning, operation status recognition models, or sensor-based local monitoring systems [7]. Although significant improvements have been made in the accuracy of single-machine operations, these methods often lack consideration of comprehensive factors such as multi-machine collaboration, operation spatiotemporal constraints, and system-level resilience, making it difficult to meet the intelligent needs of future complex agricultural ecosystems.

Against this backdrop, AI-driven cooperative control emerges as a transformative solution to these intertwined challenges. By enabling teams of autonomous tractors and supporting equipment to perceive, decide, and act in a coordinated manner, this paradigm transcends the capabilities of isolated machines. It not only directly addresses labor shortages by enabling fatigue-free multi-agent operation, but also enhances food security by improving operational efficiency and optimizing yields at scale. Furthermore, by enabling precise, location-specific resource application, it paves the way for a more sustainable and resource-efficient agricultural future.

This technological evolution is gaining strong policy and market momentum. policies such as India’s digital agriculture mission, the EU’s common agricultural policy (CAP) subsidies, and China’s smart agriculture action plan are providing strong impetus for technology implementation [8]. Driven by both policy and technology, market expansion and acceptance are rapidly increasing: the unmanned tractor market is projected to grow from US dollar 2.2 billion in 2025 to US dollar 5.2 billion in 2030 (a compound annual growth rate of 18.6%). The Asia-Pacific region will lead the global market with a 46.3% share in 2024. The annual growth rate of over 23% for fully autonomous and electric models also confirms the rapid adoption of the technology [9].

Figure 1 clearly depicts the path of technological evolution: starting from the core challenges facing agriculture, progressing through the inefficient and limited precision of manual collaborative control, and ultimately towards AI-driven autonomous operation of tractors and equipment. This diagram highlights how technology simultaneously addresses the dual challenges of production efficiency and ecological sustainability, presenting a comprehensive overview of the evolution of intelligent agricultural machinery from concept to system.

Figure 1. Diagram of the core challenges of agriculture and the evolution of cooperative control models, showing the development of tractor-implement cooperative technology from manual control to AI-driven.

This review is the first attempt to systematically examine AI-driven cooperative control technologies for autonomous tractors and implements, aiming to construct a comprehensive technical landscape. While existing research covers many specific areas [1,10,11,12,13], it has yet to achieve a systematic integration. Our discussion is guided by four interrelated technical pillars. First, we establish a collaborative intelligence foundation by embedding agricultural knowledge into AI systems and building intelligent decision-making models that conform to the physical laws of farmland [14]. We then expand this to a multi-agent collaborative framework, encompassing mechanisms for swarm intelligence formation and distributed learning methods adapted to plot separation and heterogeneous resources [15]. We focus on the physical interaction and control of tractors and implements, addressing key issues such as dynamic terrain-implement coupling, adaptive traction, and soil resistance response [16,17]. Finally, we analyze cognitive applications across different agronomic processes and comprehensively evaluate the practical effectiveness and potential of collaborative control technologies from sowing to harvest [18,19].

Our contributions are threefold:

• We formalize knowledge-embedded AI frameworks that ensure physically consistent tractor-implement interactions.
• We advance physically aware control strategies that dynamically adapt to field heterogeneity.
• We propose an integrated cooperative control ecosystem combining neuromorphic computing and FarmOS to overcome real-time coordination bottlenecks.

2. Foundational Architecture for Cooperative Intelligence

As an advanced operational paradigm in agriculture, cooperative intelligence integrates multiple actors, including autonomous agricultural machinery, a central dispatch system, sensor networks, and human-machine interfaces. Through deep integration and collaboration, the system achieves optimized resource allocation, precise task scheduling, and agile adaptation to the environment. This structured interactive system helps each unit overcome its limitations in perception and cognition, thereby building an intelligent collaborative network that spans time, space, and functions. Its operational mechanism is based on a hierarchical collaboration framework, enhanced by different knowledge layers.

2.1. Hierarchical Collaboration Framework

The proposed hierarchical collaboration framework adopts a dual-control architecture. The vertical functional dimension focuses on the core capabilities within the unit, covering perception, decision-making, and execution, building a complete workflow from data collection and task planning to precise implementation. The horizontal coordination dimension is dedicated to coordinating the cross-temporal and spatial interactions between different agents (such as tractors, drones, and central dispatch systems), focusing on resolving resource conflicts, optimizing group task allocation and scheduling, and enabling knowledge sharing through multi-agent communication, thereby comprehensively improving the efficiency of system collaboration. The framework first ensures the autonomy and functional integrity of individual agents through the vertical functional layer.

2.1.1. Vertical Functional Hierarchy

The vertical functional layer, composed of three closely interconnected sub-architectures, aims to address the core challenges of agricultural collaborative system integration by organically combining hierarchical functions with horizontal coordination mechanisms between agents. These challenges include high heterogeneity in sensory data, lack of global consistency in decision-making processes, response delays in execution, and physical constraint conflicts. To this end, this study establishes a three-layer architecture of “perception-decision-execution” as the core improvement mechanism for the vertical functional layer. As shown in Figure 2, the system integrates three modules: the perception layer is responsible for environmental perception, operation quality monitoring, and implementation status tracking; the decision layer encompasses collaborative control and task planning; and the execution layer includes power regulation, machine operation, and communication. The core goal of this design is to alleviate integration bottlenecks in multi-source information fusion, complex constraint optimization, and dynamic execution feedback through a layered mechanism, thereby establishing a closed-loop adaptive optimization capability from data to action. Ultimately, through the organic synergy of these multiple modules, the system achieves efficient, stable, and intelligent collaborative operations in dynamic farmland environments.

Figure 2. Recurrent three-layer collaborative architecture of tractors and implements.

The perception layer fuses multi-source sensor data and combines it with terrain correction technology to generate centimeter-level farmland maps. To address the unstable environmental perception issues inherent in traditional systems, which are caused by high sensor data noise and calibration errors, this study introduces multi-sensor fusion and optimization algorithms into the perception layer. For example, a high-speed precision seed drill can simultaneously utilize laser, ultrasonic, and angle sensors to operate at speeds of 12–16 km/h [20]. The collected data is first preprocessed using a Kalman filter and then optimized using an improved sparrow search algorithm (combined with an extended Kalman filter). Test results demonstrate excellent performance, with a mean absolute error of only 0.083 cm, a root mean square error of 0.103 cm, and a correlation coefficient of 0.979. This design significantly enhances the system’s ability to ensure accuracy and stability in highly dynamic operating environments. Furthermore, the high accuracy of the perception layer relies not only on advanced filtering algorithms but also incorporates prior knowledge of sensor operating principles and the physical relationships between measurements, making the perception results more reliable and more relevant to real-world applications.

The decision-making layer utilizes multi-agent reinforcement learning (MARL) and the generation of collaborative agents to collaboratively resolve conflicts within multiple agricultural constraints. Incorporating domain prior knowledge is crucial to ensure that each decision adheres to physical laws and biological mechanisms. Multi-agent reinforcement learning introduces multiple autonomous agents into complex agricultural systems, enabling them to collaborate and compete in dynamic environments to jointly optimize overall returns [21]. Unlike single-agent reinforcement learning, MARL can effectively address multi-dimensional issues in agricultural operations, such as multi-machine coordination, resource sharing, and spatial conflicts. For example, different agricultural machines operating on the same plot may need to coordinate path planning, energy allocation, and operation timing. MARL strikes a balance between global and local reward mechanisms, enabling each agent to achieve individual optimization while maintaining overall system performance. In terms of algorithm design, a centralized training, decentralized execution (CTDE) framework is often adopted. Global information is optimized through a centralized value function or policy network, and independent decision-making by each agent during the execution phase, thus balancing efficiency and stability. Some research has incorporated graph neural network (GNN) architectures to capture the interactions between agricultural machines, enabling collaborative scheduling and obstacle avoidance control. Furthermore, to address uncertainties in the farmland environment (such as climate fluctuations and surface heterogeneity), MARL models can incorporate Bayesian reinforcement learning or meta-learning mechanisms to enhance generalization and adaptability. Physical constraints such as machine range and power limits, as well as agronomic rules such as minimum irrigation requirements based on cumulative temperature and optimal sowing time, are explicitly embedded in the decision-making algorithm, making them hard constraints or goal-oriented in the optimization process. This embedding of constraints based on prior knowledge not only improves the interpretability and reliability of decisions but also ensures that each strategy output by the algorithm can be implemented under real-world physical conditions. Ultimately, the results generated by the decision-making layer can provide the execution layer with highly accurate and feasible operational instructions, driving the development of agricultural automation systems towards intelligent collaboration and adaptive control.

The execution layer precisely translates strategic decisions from the upper layer into specific actions for the tractor and implement, placing particular emphasis on motion coordination. By seamlessly integrating prior knowledge, the system ensures that each operation is physically feasible, safe, and efficient. The control architecture embeds a dynamic model of the tractor-implement system and the dynamics of soil-machine interaction, employing control algorithms such as proportional-integral-differential (PID) and multi-processor control (MPC) for precise control. Physical constraints such as the upper limit of actuator speed and the maximum safe tilt angle of the machine body, as well as expert experience such as vibration tolerance under different operating conditions, are translated into constraints or optimization objectives within the control algorithm. For example, deep soil compaction, common in rice-wheat rotations, can reach 1.01 MPa at a depth of 0–4 cm under long-term no-tillage conditions, and can even exceed 2.0 MPa at depths of 30 to 34 cm [22]. Given these operating conditions revealed by measurement data, the control strategy at the execution layer must be adjusted accordingly. Blindly adjusting tillage depth will either fail to effectively break up the compacted layer or excessively damage the soil structure. Therefore, based on prior knowledge of soil mechanics, the precision motion control module will accurately calculate the target depth range and scientific force application strategy, thereby achieving precise loosening of the soil, which not only promotes crop growth but also significantly reduces compaction damage.

2.1.2. Horizontal Coordination Hierarchy

However, the high degree of autonomy of individual agents is not the end point of cooperative intelligence in agriculture. To achieve global optimization, a horizontal coordination layer must be used to manage interactions between different types of agents and build a collaborative network that spans space, time, and functions. This system achieves global goals beyond the capabilities of a single agent through distributed algorithms and communication frameworks. In task optimization, the system employs a MARL mechanism to dynamically allocate field work areas and transportation routes, effectively resolving conflicts caused by overlapping paths and resource sharing [13]. Sequence planning, based on a spatiotemporal hierarchical model of work unit tasks [23], systematically optimizes work sequences and significantly reduces idle waiting time between machines.

To achieve the aforementioned task optimization and conflict resolution, information fusion and knowledge sharing among agents are crucial foundations. This mechanism enables agents to exchange diverse data types, including local perception information such as soil moisture distribution, optimization decisions such as route updates, and learning models such as terrain traction characteristics. However, this frequent data exchange places high demands on the communication system. Given limited communication bandwidth, the system combines an event-triggered mechanism with edge cloud collaborative computing to effectively enhance overall environmental perception capabilities and lay the foundation for subsequent consensus building.

The theoretical basis of this coordination mechanism stems primarily from the distributed optimal control method [24]. Its core concept is to decompose global objectives, such as reducing total energy consumption or shortening total operation time, into multiple interconnected local subproblems. Agents collaborate through boundary coupling variables (such as shared virtual paths, resource availability signals, or consensus states). The system also achieves consensus building and conflict resolution by generating collaborative agents or using distributed optimization methods. This system coordinates individual agent objectives with global agricultural rules (such as shared operation schedules and farm energy conservation requirements [25]), ultimately ensuring the consistency and efficiency of the overall system operation.

However, practical applications still face challenges such as communication latency, heterogeneous interface compatibility [26], and fault tolerance requirements. Addressing these challenges requires robust communication protocols that embed knowledge of system operational constraints to achieve stable and reliable coordinated control.

In summary, the horizontal coordination hierarchy aims to address the interaction issues between multiple agents. Achieving its ideal effectiveness relies on a comprehensive set of theories and methodologies. The challenges discussed above, such as communication latency, heterogeneous compatibility, global goal decomposition, and conflict resolution, essentially constitute a typical multi-agent system (MAS) coordination problem. Therefore, it is necessary to examine and address these challenges from a more macroscopic and systematic perspective: the multi-agent coordination framework (MACF). This framework encompasses core elements such as collective architecture, learning mechanisms, and task orchestration, as detailed below.

This dual-control architecture adheres to the core principle of “high-level goals guiding low-level actions, while low-level feedback optimizes high-level decisions”. The key lies in the continuous embedding of physical, biological, and agricultural operational knowledge into the agent’s functional units and coordination mechanisms, ensuring that feedback loops operate stably within feasible production constraints and enabling collaborative operations to effectively span spatial and temporal scales [27]. For example, in multi-machine collaborative operations, a cluster of agricultural machines implements task sequence planning and conflict resolution through a horizontal coordination layer, significantly reducing idle time [23]. Each machine, however, relies on its own vertical architecture (perception, decision-making, and execution) to accurately complete its assigned subtasks. Embedded knowledge, such as machine performance, field management rules, and soil characteristics, jointly shapes the operating rules and constraints at both levels.

2.2. Knowledge-Embedded AI Foundations

To achieve its vision of “intelligent collaboration”, the aforementioned hierarchical collaboration framework relies on underlying AI technology. However, purely data-driven AI models often face semantic barriers, decision-making distortion, and insufficient adaptability in the complex physical environment of agriculture. Therefore, knowledge embedding is key to improving system credibility, interpretability, and practicality. Its theoretical foundation is hybrid modeling, which integrates three complementary modeling approaches: mechanistic physics models, symbolic models, and data-driven agent models. Mechanistic physics models include Newtonian dynamics [28], which describe the laws of mechanical motion, and soil-agricultural machinery interaction models, which simulate tillage resistance [29]. Symbolic models encompass crop growth calendars that guide the sequence of agricultural operations and irrigation strategies based on moisture thresholds. Data-driven agent models, trained with sensor data, can capture complex dynamics that are difficult to describe using other methods [30].

To clearly compare the characteristics of different methods, Table 1 compares the actual effects of various knowledge embedding strategies from three dimensions: system architecture, application scenarios, and performance.

Table 1. Comparison of Knowledge-Embedded AI Methods.

Method Type	Implementation Mechanism	Application Case	Performance
Knowledge-Guided Loss Functions	Embed physics rules (e.g., engine power-fuel relationships) into neural network loss terms	Tractor tillage depth control [31]	Guides to “high-power, low-fuel” regions
Physically Constrained RL (PC-RL)	ECMS + fuzzy PI for dynamic battery equivalence factors	Hybrid tractor energy mgmt [25]	6.71% fuel reduction
Differentiable Mechanism Modules	Kalman filter for online angle mapping calibration	Tillage depth detection [32]	Enhanced generalization
Physics-Informed Neural Nets (PINNs)	Incorporate soil-implement interaction PDEs as network constraints	Draft force prediction [33]	17.3% accuracy improvement
Symbolic Knowledge Injection	Encode agronomic rules (e.g., crop rotation constraints) into policy networks	Multi-season planning [34]	Reduced constraint violations by 42%

From a system architecture perspective, the effectiveness of knowledge embedding depends not only on the strategy itself but also on its synergistic effect within the overall system. The three-tiered vertical functional framework for intelligent agricultural machinery, shown in Figure 3, embodies this synergistic concept. This framework demonstrates that while the core role of knowledge embedding is concentrated at the decision-making level, its value permeates the entire system: the perception layer improves data quality by embedding sensor physics knowledge; the decision-making layer integrates agronomic and physical constraints for optimization; and the execution layer incorporates an understanding of organizational dynamics to achieve precise control.

Figure 3. Knowledge-embedded intelligent decision making architecture for agricultural machinery systems.

However, layered design alone is insufficient to address the structural weaknesses of model integration. In this framework, the introduction of formalized agronomic constraints and mechanical dynamics models is crucial for achieving credible decisions and feasible control. Its design is theoretically highly consistent with the “constrained reinforcement learning” framework proposed by Liu et al. [35]. Both approaches explicitly incorporate agronomic and physical constraints into the decision-making optimization process to avoid the mismatch between semantic understanding, physical consistency, and safety feasibility inherent in data-driven models.

Specifically, the constrained reinforcement learning framework models agricultural autonomous control as a constrained Markov decision process. By introducing immediate and cumulative constraints into the reward function, the model is able to achieve operational objectives while satisfying agronomic constraints such as energy consumption, safety, and crop protection. This mechanism provides theoretical support for the integration of formalized agronomic constraints and mechanical dynamics. Correspondingly, in this research framework, formalized agronomic constraints guide operational logic (e.g., matching crop growth stage with tillage depth), while the mechanical dynamics model constrains energy transfer and motion response through differentiable equations, ensuring that the action strategy converges within the physically feasible domain.

This design, which embeds agronomic constraints and dynamics laws into the decision-making process, essentially implements a “constrained reinforcement learning”-style knowledge embedding. By imposing explicit feasibility conditions within the AI reasoning chain, the system ensures that the learning results converge under the dual constraints of agronomic semantics and physical consistency, significantly improving the model’s stability, interpretability, and security. This echoes the constrained optimization concept proposed by Liu et al. [35] in automated agriculture, validating the theoretical feasibility and generalization potential of this framework in complex agricultural environments.

Thus, the combination of formalized agronomic constraints and mechanical dynamics not only provides an algorithmic optimization mechanism but also forms a cross-level coordination structure, enabling perception, decision-making, and execution to work together in a unified physical and agronomic semantic space. This design is a key path to realizing “intelligent collaborative” agricultural AI systems.

The practical effectiveness of the knowledge-embedded AI framework based on the integration of agronomic constraints and mechanical dynamics is verified through the following application cases. For tractor plowing depth control, researchers embed engine load characteristics as additional constraints into a neural network prediction model, enabling the system to significantly reduce fuel consumption while maintaining high operating power [31]. Another study, focusing on hybrid tractor energy management, develops a learning system that incorporates physical rules. This system introduces a fuzzy PI controller into the equivalent fuel consumption minimization strategy (ECMS). By dynamically adjusting the equivalent factor to ensure the battery state of charge (SOC) remains within a reasonable range, it successfully reduces fuel consumption by 6.71% [25]. Further research employs a differentiable mechanics model for plowing depth control and combines it with a Kalman filter for online calibration, strengthening the correlation between implement angle and actual cutting depth and significantly improving system reliability [32]. These examples demonstrate that knowledge embedding can effectively address the shortcomings of purely data-driven models and enhance the reliability and efficiency of systems in real-world physical environments.

3. Multi-Agent Coordination Framework

As discussed in Section 2, the horizontal coordination mechanism of agricultural cooperative intelligence inherently requires orderly collaboration among multiple agents. This requirement creates an urgent need for systematic coordination theories and methods. The MACF serves as the theoretical foundation for this purpose. It transcends the autonomous capabilities of individual agents and focuses on managing swarms of heterogeneous equipment, such as tractors, drones, and harvesters, enabling them to function as a coordinated team, truly realizing swarm intelligence where the whole is greater than the sum of its parts. Farms increasingly rely on collaborative teams comprised of various intelligent machines, such as tractors, crop-spraying drones, and harvesters, to achieve higher efficiency with less investment. The MACF forms the key theoretical foundation for the efficient coordination of these systems, making complex swarm operations possible. This framework integrates three core capabilities: onboard autonomous decision-making, orderly knowledge sharing between devices, and flexible and adaptable dynamic task scheduling. This allows agricultural swarms to effectively adapt to changing field environments, diverse agricultural tasks, and unevenly distributed resource allocation [36,37]. Research has shown that this collaborative mechanism significantly outperforms traditional single-machine decision-making models, paving the way for building next-generation intelligent farm systems capable of self-adjustment and overall optimization.

To address specific weaknesses in traditional agricultural system integration, such as information silos, delayed decision-making, and unbalanced resource scheduling, the MACF design emphasizes a distributed multi-agent collaborative optimization mechanism. By combining a modular architecture with a highly reliable communication protocol, the system enables efficient task decomposition, state sharing, and real-time feedback across different types of agricultural machinery, overcoming the bottlenecks of single-center control architectures in complex and dynamic environments. This design not only improves the system’s scalability and robustness but also provides a unified interface and coordination logic for subsequent collective intelligence architectures and task orchestration systems.

For example, in the scenario of coordinated fertilization and field logistics scheduling, the framework integrates a dynamic task allocation model with execution time and distribution distance as joint optimization objectives. Practical applications have shown that in field trials conducted in Jimo district, Qingdao, the introduction of the chaos-cauchy fireworks algorithm (CCFWA) significantly reduced resource distribution variance by over 48% compared to traditional methods [15]. This not only enables precise seed and fertilizer application, but also effectively improves agronomic coordination efficiency by balancing nutrient supply.

3.1. Collective Intelligence Architecture

Collective intelligence architecture is a concrete blueprint for realizing the horizontal coordination layer’s goals of “building a cross-temporal, cross-functional intelligent collaborative network” and “achieving global goals through distributed algorithms”. Within the framework of multi-agent coordination, collective intelligence architecture specifically refers to the systematic control logic and communication interaction paradigm designed to enable the collaborative operation of heterogeneous agricultural agent clusters, namely multiple self-driving tractors and various intelligent agricultural machinery attachments. This architecture treats each independent agricultural machine as an autonomous agent, sharing real-time status and environmental data through high-speed communication networks and relying on AI algorithms for distributed decision-making, ultimately enabling the entire cluster to exhibit globally optimized collaborative behavior. Its core research focuses on exploring different coordination mechanisms, such as centralized control, distributed negotiation, or hybrid decision-making, to improve the system’s operating efficiency, task adaptability, and overall robustness in complex farmland environments, thereby laying the core theoretical foundation for the realization of large-scale unmanned farms. The collective intelligence architecture was designed to further optimize coordination delays and data consistency issues common in distributed systems. By introducing an adaptive synchronization protocol and a multi-level communication topology, the system maintains efficient collaboration among agents even in environments with fluctuating networks or limited bandwidth. Furthermore, a shared situational awareness model based on a knowledge graph enables diverse agents to form a unified understanding of the environment and collaborative decision-making foundation despite heterogeneous sensors and execution units, fundamentally alleviating information asymmetry and task conflicts.

The collective intelligence architecture, built on an efficient and reliable communication network and a shared situational awareness model, enables heterogeneous tractors and agricultural implements to exchange multi-source sensory data in real time, such as soil moisture distribution maps and crop growth images, to jointly construct and continuously update a unified global field operation model [13]. Based on this, the system’s decision-making unit can adopt various design approaches, such as a centralized planner, distributed negotiation, or a hybrid hierarchical model. It dynamically decomposes macro-agricultural goals (such as completing a full field harvest before rain or minimizing total system energy consumption) into a series of executable subtasks, precisely allocating tasks based on the capabilities and status of each agent [13]. In practice, multiple machines complete complex agronomic tasks through rigorous collaborative motion planning and operation sequencing. Relying on continuous environmental feedback and internal state interaction, they achieve real-time closed-loop optimization of perception, decision-making, and execution.

This collaborative strategy has achieved remarkable results. In areas with dispersed fields, the system deeply integrates multi-source maps, positioning information, and real-time machine status to dynamically generate coordinated and optimized operating paths, significantly improving the overall efficiency of the fleet. Research has shown that collaborative optimization significantly improves overall operational efficiency by optimizing tasks across the entire system and reducing idle time [37]. This fully demonstrates the practical value of distributed intelligent scheduling in modern agriculture.

However, the successful operation of this mechanism still faces many limitations, particularly due to the highly heterogeneous agricultural environment. Interface differences between different types of agricultural machinery must be overcome, and the reliability and real-time nature of data communication must be ensured within the limited bandwidth of the field [38]. Furthermore, the accuracy and interoperability of sensory data (for example, identifying plot boundaries using machine vision) directly impact the effectiveness of group coordination and operational strategy execution. Currently, how to achieve a consistent understanding of sensor data among different agents and to develop efficient and reliable multi-source information fusion mechanisms remain key research areas of widespread concern [38].

The collective intelligence architecture establishes the system structure and interaction principles for multi-agent collaboration. To achieve the adaptive behavior and strategy optimization of intelligent agents on this architecture, it is necessary to rely on the cooperative learning mechanism to provide algorithmic support.

3.2. Cooperative Learning Mechanism

Collaborative learning mechanisms, such as MARL, are core technologies for implementing distributed optimization methods and cooperative strategy generation at the horizontal coordination layer. These methods enable groups of agents to optimize operational strategies by sharing knowledge and exchanging experiences, thereby achieving swarm-level cooperative evolution, far exceeding the efficiency of single-agent learning. Through distributed decision-making and cooperative learning, these methods enable intelligent systems to adaptively cope with the uncertainties of complex agricultural environments.

Despite its significant advantages, cooperative learning still faces multiple challenges in practical agricultural applications. The primary challenge stems from unreliable communication environments. Fluctuations in field networks and uneven signal coverage can introduce significant communication delays and even packet loss. This makes it difficult for agents to synchronize models or gradient updates in real time. Decisions are based on outdated and inconsistent global information, ultimately causing policy drift and seriously damaging the overall cooperative effectiveness of the system. Another key challenge lies in data heterogeneity. Different operating units have different local environments (such as soil type, crop density, and agricultural machinery load), resulting in completely different distributions of the data they collect. This non-independent and identically distributed data not only slows down the convergence of a unified optimal strategy, but can also cause policy conflicts between agents, affecting the overall cooperative effect. In addition, resource constraints and safety requirements in agricultural scenarios further increase the complexity of the design of cooperative learning algorithms.

Several innovative solutions have been proposed to address these challenges. Xie et al. propose a switching system framework designed to mitigate network latency and packet loss, which significantly improves the scalability and robustness of systems in dynamic network environments [39]. Building on the enhanced network stability of this framework, Li and Wei further explore the challenges posed by data processing and heterogeneity, and propose an output synchronization control method based on data-driven and adaptive dynamic programming [40]. This method effectively improves the system’s adaptability to heterogeneity and external interference. Together, they enhance the overall performance of complex network systems from both the communication and data processing levels.

Building on this foundation, federated learning (FL), a distributed collaborative learning framework, enables multi-agent collaborative training while protecting data privacy and is gradually being introduced into the collaborative learning process of multi-agent systems. The core concept of FL is “local training − parameter update − global aggregation”. Specifically, each agent independently trains a model locally using its own sensory data, then uploads only the model parameters or gradients, not the original data. A central coordination node or aggregation mechanism generates a global model, which is then distributed back to each agent to achieve knowledge sharing. Specifically, each agricultural agent (such as a tractor, drone, or harvester) trains its model locally and periodically sends model updates to a global aggregator. The global aggregator generates a global policy using algorithms such as federated averaging and distributes the updated model to each agent, achieving cross-device policy synchronization and collaborative optimization.

This mechanism significantly reduces communication overhead, avoids the privacy risks associated with centralized transmission of sensitive data, and improves the system’s learning stability in low-bandwidth and highly heterogeneous environments. In agricultural scenarios, FL enables smart devices distributed across different plots and facing diverse environmental conditions to perceive differentiated data locally while aggregating a global model to form a shared optimal strategy, effectively addressing the uncertainty of field tasks and uneven data distribution.

In recent years, researchers have further proposed the federated multi-agent reinforcement learning (FMARL) framework, combining the privacy-preserving and distributed collaboration advantages of FL with the multi-agent decision-making capabilities of MARL. This framework enables agents to perform reinforcement learning training locally while simultaneously enabling cross-agent collaboration through global policy aggregation, thereby improving overall learning efficiency and decision robustness [41]. This approach not only ensures system scalability and privacy security but also accelerates the convergence of collaborative strategies in complex agricultural networks, providing a feasible path for the practical application of multi-agent collaborative learning.

It’s worth noting that current research still has some limitations. Most algorithms have been validated only in simulation environments and lack the support of large-scale field experiments. Furthermore, existing methods require high computing resources, making their deployment on resource-constrained agricultural equipment challenging. Future research could focus on lightweight algorithm design, optimizing the communication-computation trade-off, and ensuring security and fault-tolerance mechanisms.

The cooperative learning mechanism realizes the collaborative optimization of intelligent agent strategies. However, in order to convert the optimized strategies into specific executable task sequences and coordinate multiple intelligent agents to jointly achieve complex goals, the specific scheduling and management of the task orchestration system (TOS) is required.

3.3. Task Orchestration System

The TOS is the core implementation vehicle for functions such as “precise task scheduling”, “optimal resource allocation”, “sequence planning”, and “conflict resolution” in the horizontal coordination layer. It aims to systematically address the aforementioned coordination challenges. As a core component of the MACF, the TOS’s core function is to transform complex global missions into executable collaborative action plans. Specifically, it parses and dynamically decomposes high-level goals, generating a series of dependent subtasks or atomic behaviors. These tasks are then efficiently and rationally assigned to heterogeneous agent members based on the agent’s state and capability set. Furthermore, the system monitors and schedules the entire task execution process in real time, replanning and handling exceptions in the event of uncertainties such as agent failures, task timeouts, or sudden environmental changes, ultimately ensuring robust and efficient completion of the overall task [42]. In the hierarchical architecture of MAS, the TOS plays a critical role in connecting the upper and lower levels. It serves as both the “executive arm” of high-level decision-making and planning modules, translating macro-strategies into operational instructions; and the “coordination hub” of the autonomous control units of the underlying agents, integrating the dispersed individual behaviors to generate coordinated swarm intelligence [43]. Therefore, its performance directly determines the overall collaborative effectiveness and adaptability of the MAS in complex and dynamic scenarios. However, designing an efficient TOS faces numerous core challenges, with research focusing primarily on resolving the interconnected problems of “decomposition-allocation-scheduling-coordination”.

3.3.1. Centralized Orchestration Methods

Centralized task orchestration methods utilize a single control node architecture and rely on a central controller to make global task decomposition, allocation, and scheduling decisions. These methods typically employ global optimization models, applying mathematical programming methods (such as mixed-integer linear programming, MILP) or market-based algorithms (such as the consensus bundling algorithm, CBBA) to solve the model and theoretically achieve the optimal task allocation. Based on complete system state information (including the capabilities of all agents and task requirements), the central controller generates specific execution instructions through an optimization algorithm. This approach offers the advantage of ensuring consistent and globally optimal system behavior, making it particularly suitable for scenarios with complex inter-task dependencies and the need for high levels of coordination. However, this approach also has significant limitations. For example, Peng et al. construct a multi-agent architecture and MILP framework based on deep reinforcement learning, which, while improving the efficiency of allocating large-scale and concurrent tasks to a certain extent, still faced challenges such as high model training data requirements and poor interpretability [44]. Ye et al. employ a CBBA to address multi-task allocation with task coupling constraints, demonstrating its feasibility in specific scenarios such as search and rescue [45]. However, such methods are generally applicable only to environments with fixed tasks or fixed resource information, and struggle to cope with dynamic task scheduling requirements. Bi et al. model UAV formation force allocation as an integer linear programming problem and proposed a distributed multi-UAV task reallocation algorithm [46]. While this algorithm mitigated the impact of platform losses on task execution to some extent, it still failed to effectively address the coordination deficiencies and inefficiencies caused by resource competition and synergy. Furthermore, centralized approaches also suffer from high risk of single-point failure of central nodes, large communication bandwidth requirements, and limited system scalability. In dynamic environments, the computational overhead of resolving the optimization problem is significant, making it difficult to meet the requirements of applications with high real-time requirements, further limiting their application in complex real-world systems.

3.3.2. Distributed Orchestration Methods

Distributed task orchestration methods employ a decentralized agent architecture, achieving task coordination through local interactions and autonomous decision-making, without relying on a central control node. These methods are typically based on distributed algorithmic design, such as negotiation mechanisms like the contract net protocol (CNP) or consensus algorithms (such as the CBBA) to achieve inter-agent coordination and self-organization. Under the assumption that each agent relies solely on local observations and has a limited communication range, they gradually reach a consensus on task allocation through multiple rounds of iteration and interaction with neighbors. Distributed architectures offer excellent fault tolerance and system scalability, effectively addressing dynamic changes in network topology and single points of failure, making them particularly suitable for highly dynamic environments such as vehicular networks. Furthermore, since they do not require global information sharing, distributed methods offer significant advantages in privacy protection, avoiding the security risks associated with the centralized transmission of sensitive data. However, these methods also have significant limitations. For example, Agrawal et al. employ deep MARL to handle multi-robot task allocation in dynamic logistics warehouses [47]. While this approach demonstrates good local responsiveness, it lacks a global optimization perspective, which can easily lead to resource allocation conflicts and overall performance fluctuations. Liu et al. propose a graph neural network-enhanced deep reinforcement learning scheme, which shortens task completion time by establishing a Markov decision process [48]. However, without an explicit coordination mechanism, the overall system efficiency is still limited. Some studies attempt to combine federated learning frameworks to aggregate policy gradients in a distributed manner, relying solely on local observations to achieve joint sub-channel and power optimization for vehicle-to-vehicle links. While this approach significantly reduces communication overhead, it still does not fully address spectrum contention and model convergence stability issues in high-density scenarios. In general, despite the good scalability and privacy protection capabilities of distributed approaches, they typically converge only to suboptimal solutions, incur high communication overhead during negotiation, and struggle to guarantee convergence speed and final decision quality in scenarios with complex task dependencies and highly dynamic environments, limiting their application in systems with high real-time requirements.

3.3.3. Hierarchical Orchestration Methods

Hierarchical task orchestration methods employ a hybrid control architecture, combining the advantages of centralized global coordination with distributed local autonomy. They are typically designed as a two- or multi-layer control structure. Upper-layer controllers (such as cloud centers or domain master nodes) are responsible for macro-task planning, cross-domain resource coordination, and coarse-grained task allocation. Lower-layer agents (such as edge servers or end devices) perform fine-grained task scheduling and real-time adaptive adjustments based on local environmental information. Typical implementations include leader-follower and federated architectures. In these approaches, upper-layer nodes formulate global task decomposition and resource allocation strategies, while lower-layer nodes execute specific tasks while maintaining a certain degree of autonomy and can dynamically respond to local state. This approach demonstrates significant advantages in cloud-edge-end collaborative computing. For example, Cai et al. propose a three-tier collaborative computing architecture centered on mobile edge computing [49]. By constructing a “large-small resource tree” model, they achieve multi-layer resource awareness and unified management. Task segmentation and preliminary scheduling are performed at the edge layer, while global decision-making is made at the cloud layer. This approach improves resource utilization efficiency while maintaining system scalability. Cai et al. utilize long short-term memory for resource prediction and a double deep Q network-based hierarchical scheduling mechanism (LST-DDQN) [49]. This enables efficient parallel offloading and execution of tasks across the cloud, edge, and end, significantly reducing task completion latency and energy consumption. Despite this, layered architectures also face challenges such as high design complexity, difficulty demarcating inter-layer responsibility boundaries, and difficulty developing communication protocols. Failures in high-level control nodes can still impact overall scheduling performance. Furthermore, achieving policy consistency and information synchronization across different layers, particularly in highly dynamic mobile environments such as vehicular networks, remains a key research challenge.

3.3.4. Learning-Based Intelligent Orchestration Methods

Learning-based intelligent task orchestration methods primarily leverage machine learning techniques, particularly MARL and graph neural networks (GNN), to autonomously learn and optimize task allocation policies through a data-driven approach. These methods typically employ a “centralized training, distributed execution” framework, such as the multi-agent deep deterministic policy gradient (MADDPG) algorithm. During training, global state information is used to optimize the policy network. During execution, each agent relies solely on local observations for decision-making, thus balancing the requirements of global coordination and local autonomy.

In recent years, these methods have demonstrated significant advantages in complex task allocation. The multi-agent system designed by Peng et al. via deep reinforcement learning, paired with an automated MILP framework, accelerates the processing of extensive parallel workloads [44]. A deep MARL approach introduced by Aakriti Agrawal coordinates task assignment among multiple robots in dynamic logistics warehouses [47]. A graph-augmented deep reinforcement learning (GA-DRL) scheme, proposed by Liu et al. through the combination of graph neural networks and reinforcement learning, leverages Markov decision process modeling to effectively shorten task completion time [48]. Li et al. improve the multi-agent deep deterministic policy gradient algorithm, improving the rationality of task allocation while reducing decision complexity [50]. These methods are capable of handling high-dimensional state spaces and dynamic environmental changes, avoiding complex manual rule design in an end-to-end manner, and exhibiting strong generalization and environmental adaptability. However, they still face several challenges: first, they require a large amount of training data and computing resources, resulting in an unstable model training process; second, the learned policies are difficult to interpret, making reliability verification and system certification difficult in safety-critical scenarios such as military command and emergency response. Therefore, while learning-based approaches offer new solutions for multi-agent task orchestration, their theoretical completeness and practical implementation still require further exploration and improvement.

To systematically compare the characteristics of mainstream task orchestration methods, Table 2 compares four typical approaches: centralized orchestration, distributed orchestration, hierarchical orchestration, and learning-based intelligent orchestration, across seven key areas: control structure, scalability, robustness, optimality, communication overhead, typical use cases, and core challenges. This comparison is an important reference for selecting appropriate orchestration strategies in complex system coordination scenarios, such as intelligent agricultural machinery systems.

Table 2. Comparison of Four Task Orchestration Methods.

Aspect	Centralized Orchestration	Distributed Orchestration	Hierarchical Orchestration	Learning-Based Intelligent Orchestration
Control Structure	Single central control node, star topology	Fully decentralized, mesh topology	Hybrid architecture (typically two or more layers), upper layer for centralized coordination, lower layer for distributed autonomy	Often uses a “Centralized Training, Decentralized Execution (CTDE)” framework; training centralized, while execution is distributed.
Scalability	Poor	Excellent	Good	Medium
	The central node becomes a bottleneck; communication and computational pressure increase dramatically as nodes are added.	No central bottleneck; nodes can freely join/exit, making it easy to scale the system.	Hierarchical management alleviates pressure on the top layer; scalability is between that of centralized and distributed methods.	High computational overhead during training; good scalability during execution. However, environmental changes may require re-training, affecting agility.
Robustness	Poor	Excellent	Medium	Design-Dependent
	Single point of failure; system failure occurs if the central node fails.	No single point of failure; local failures do not affect the whole system; strong self-organization capabilities.	Failure of the upper layer affects global coordination, but the lower layers can still operate autonomously; can be highly robust if well-designed.	Depends on the training environment and generalization capability; may perform poorly with unseen failures or abrupt environmental changes.
Optimality	Excellent (Theoretical)	Suboptimal	Good to Excellent	Uncertain
	Optimizes based on global information, easily achieving a globally optimal solution.	Decisions based on local information, usually converging to a suboptimal or satisfactory solution.	Macro-optimization at the upper layer with local adjustments at the lower layer; often approaches global optimality.	Data-driven approach approximates an optimal policy; performance highly depends on training quality. May find efficient solutions but does not guarantee optimality and is hard to verify.
Communication Overhead	High	Medium	Variable	Low at Execution
	All information must be uploaded to the center, and commands must be downloaded, requiring high bandwidth.	Frequent interaction and negotiation between neighbors, requires multiple iterations; total communication volume can be significant.	Inter-layer + intra-layer communication; designed to reduce unnecessary data uploading, typically lower overhead than purely centralized methods.	Only local observation is needed during execution, resulting in low overhead. However, training phase requires centralized data or model interaction, incurring massive overhead.
Typical Use Cases	Scenarios with complex task dependencies requiring high coordination, relatively static environments, and extremely high demand for global optimality.	Dynamically changing network topologies (e.g., VANETs), high fault tolerance requirements, high privacy needs, scenarios where ultimate optimality is not critical.	Cloud-edge-device collaborative computing, cross-domain collaboration, large-scale heterogeneous systems (e.g., IoT, UAV swarms).	Highly dynamic and complex environments, high-dimensional state spaces, scenarios difficult to model with traditional rules (e.g., complex games, adaptive network optimization).
Core Challenges	Single point of failure; high computational overhead; poor scalability; difficulty coping with highly dynamic environments.	Prone to local optima; significant communication overhead during negotiation; difficulty ensuring global consistency; slow convergence with complex task dependencies.	High system design complexity; designing inter-layer interfaces and protocols; maintaining consistency between global and local strategies; avoiding the upper layer becoming a bottleneck.	High demand for training data and computing resources; poor interpretability of policies; difficulty in reliability verification; unstable training process.

Overall, research on multi-agent TOS has evolved from centralized control to distributed collaboration, and finally to hierarchical hybrid architectures, with the field now thriving towards intelligentization. Each approach has its own unique characteristics: centralized approaches pursue global optimality but suffer from limited scalability; distributed approaches prioritize robustness but face a trade-off between solution quality and communication overhead; hierarchical approaches achieve a good balance in practice; and learning-based approaches demonstrate significant potential for addressing complex problems. Currently, this field faces several key challenges. First, existing research largely assumes homogeneous agents, leaving in-depth research on coordination mechanisms for heterogeneous systems. Second, as system scale expands, ensuring orchestration quality while maintaining low computational and communication complexity presents a significant challenge. Third, the reliability and interpretability of learning-based approaches in safety-critical scenarios remain to be improved. Furthermore, achieving hybrid-enhanced orchestration for human-machine collaboration and addressing the migration from simulation to reality are crucial areas requiring urgent breakthroughs. Future research should focus on adaptive allocation theory for heterogeneous systems, lightweight distributed consensus algorithms, learning frameworks under security constraints, human-machine collaborative decision-making paradigms, and simulation migration technologies. Through multidisciplinary integration, we can ultimately achieve an efficient, reliable, and scalable intelligent TOS.

4. Physics-Aware Implement Control

Although the multi-agent collaboration framework lays the foundation for machine clusters, achieving precise on-site operations requires complex implementation controls to address the core challenge of perceptual robustness under environmental interference.

This section elaborates on the technical pillars of intelligent implement control: dynamic tractor-implement modeling, adaptive control, and standardized security pairing, each providing essential support for advancing smart agriculture. Dynamic tractor-implement modeling lays the foundation for understanding the physical characteristics of the system by constructing high-fidelity digital twins. Adaptive control takes this model as its core, endowing agricultural machinery with the decision-making and execution capabilities to deal with complex environments, and it is the core link to achieve precise autonomous operation. Standardized security pairing, as the ultimate guarantee, ensures that the above-mentioned intelligence can be reliably and credibly executed in the collaboration of heterogeneous devices.

4.1. Dynamic Tractor-Implement Modeling

Dynamic modeling is the foundation for dealing with environmental disturbances and enhancing the robustness of systems. It converts unmeasurable environmental disturbances into predictable model parameters and state disturbances by constructing high-fidelity dynamic models, thereby providing crucial forward-looking and compensation basis for subsequent adaptive control.

Modeling the dynamic interaction between tractors and implements is crucial, as machinery often consists of complex multibody components and operates in uncertain environments. Traditional nonholonomic constraint models cannot capture this complexity [51]. To improve accuracy and computational performance, Redon et al. introduce an adaptive joint selection mechanism that rigidifies less critical joints, ensuring that the computational load increases with the number of kinematic joints [52]. Building on this, Gayle et al. introduce hierarchical collision detection, using blended bounding boxes to quickly eliminate inactive regions and utilizing a custom Jacobian matrix to reduce the complexity of the collision response to a sublinear level [53]. In light of the current research status and actual demands, for agricultural implements directly connected to tractors, their interaction modeling can be further simplified: there is no need to construct a dedicated high-dimensional complex interaction model. Instead, it can be abstracted as a command-response system and embedded in a reinforcement learning framework for collaborative optimization [54]. Specifically, the tractor, as the main body of intelligent control, sends action instructions to the farm tools, which then form response states based on sensor feedback. Reinforcement learning agents, through continuous interaction with the environment, autonomously learn the dynamic mapping relationship between commands and responses, thereby achieving efficient control and task optimization of tractor-farm tool systems without the need for explicit modeling of farm tool dynamics.

In addition to kinematics, accurate traction force prediction is crucial for energy-saving control and precise trajectory planning. Mechanical modeling based on Buckingham II theorem abstractions 13 parameters into 5 dimensionless groups, simplifying complexity while maintaining physical consistency [55], by optimizing tire pressure to 80 kPa and operating under low soil moisture conditions, the traction efficiency can be significantly improved [56]. These findings provide a basis for the development of intelligent decision-making systems [57]. Meanwhile, intelligent algorithms such as artificial neural networks have made breakthroughs in prediction accuracy [58]. It provides a new idea for the prediction of traction force under complex working conditions.

In the field of tractor transmission system optimization, by using computer simulation technology to analyze the structural characteristics and energy consumption performance of the transmission system, a theoretical basis can be provided for subsequent optimization. Experimental verification link focuses on the actual performance of power output device, such as continuous working ability and work quality key indicators [28,59]. Intelligent algorithms are widely applied in parameter optimization, achieving performance improvement by balancing multi-objective constraints [60]. It is particularly worth noting that the development of the new type of electromechanical hydraulic composite transmission system provides an innovative solution for power distribution, and its modular design concept significantly enhances the system’s adaptability [25].

4.2. Adaptive Control Strategies

Dynamic modeling provides a theoretical basis for tractor control. However, the agricultural environment is highly variable, which poses a challenge to the reliability of precise models. Fixed control parameters cannot adapt to all working conditions, which is precisely a manifestation of the lack of system robustness. Therefore, modern intelligent agricultural systems adopt adaptive control strategies to enhance accuracy and robustness under complex conditions. As shown in Figure 4, we advance physically aware control strategies that dynamically adapt to field heterogeneity. By optimizing task allocation and path optimization through intelligent algorithms at the strategic level, achieving precise trajectory planning and real-time adjustment through model predictive control (MPC) at the tactical level, and ensuring real-time regulation through adaptive PID and neural network control at the execution level, we can achieve centimeter-level positioning accuracy in complex farmland environments and significantly enhance operational efficiency and system reliability. The feasibility of this adaptive strategy has been widely supported. Bhat and Wang point out that AI and adaptive control are the key trends to deal with the uncertainty of the agricultural environment [26], while Che et al.’s field experiments confirm that such strategies can achieve centimeter-level path tracking accuracy [61].

Figure 4. Adaptive control strategies for tractors and implements.

Strategic level: The discrete fireworks algorithm [15] effectively solves the problems of task allocation and path optimization in multi-machine collaborative operations through chaotic mapping initialization and Cauchy mutation operations. The improved genetic algorithm [62] adopts two-segment encoding and adaptive crossover operators to address the issues of load imbalance and path crossover in the allocation of large-scale farmland tasks. The improved ant colony algorithm [63] introduces a dynamically updated pheromone matrix, which not only solves the problem of uneven agricultural machinery load caused by “nearby allocation”, but also realizes efficient task reallocation in a dynamic environment.

Tactical level: MPC combined with robust controllers facilitates trajectory planning and real-time adjustment. For articulated tractors navigating orchard alleys, prediction and control horizons are adaptively optimized via genetic algorithms, markedly reducing path-tracking deviations [64]. In four-wheel-independent-drive self-steering vehicles, model predictive control - direct yaw-moment control (MPC-DYC) architectures achieve precise synchronization between front and rear wheels, minimizing crop damage [65]. Additionally, a layered architecture (MPC for outer-loop planning, sliding mode control for dynamic tracking, nonlinear disturbance observer for disturbance rejection, and prescribed performance control for error bounds) maintains positional accuracy under sudden loads within 0.1 m [66].

Execution level: Real-time regulation is achieved through adaptive PID and neural PID controllers. The fuzzy self-tuning PID in the dual-loop scheme reduces the heading and lateral errors in trajectory tracking [67], while the single-neuron adaptive PID (SNA-PID) applies supervised Hebbian learning, combined with dead zone and gain adjustment mechanisms, improving vertical stability on different terrains [68].

This orchestrated framework empowers agricultural machinery with centimeter-level positioning precision even in challenging field conditions, significantly boosting operational efficiency and reliability. It thus represents a key enabler for scalable, resilient, and intelligent agriculture.

4.3. Standardized Secure Pairing for Farm Equipment

Adaptive control strategies endow agricultural machinery with core intelligence to deal with complex environments. However, to reliably transform this intelligence into productivity in a heterogeneous device collaborative network, it is necessary to establish a standardized security pairing mechanism as the underlying guarantee.

The current blockchain-based internet of things (IoT) security technology is evolving in a diversified manner. Table 3 compares four leading mechanisms for secure pairing and integration across heterogeneous agricultural equipment. The automated verification of smart contracts enables the automation of the entire lifecycle management in cross-brand device pairing [69]. A lightweight ECC encryption approach combined with blockchain anchoring enhances the security performance for devices with limited computational capabilities [70]. Zero-knowledge proof (ZKP) effectively addresses the core contradiction in equipment leasing scenarios, where it is essential to verify the legitimacy of the equipment while preserving the confidentiality of the manufacturer’s algorithms [71,72]. Furthermore, multimodal biometric features strengthen anti-tampering capabilities through physical-layer feature authentication [36].

Table 3. Comparative Analysis of Secure Pairing Mechanisms for Heterogeneous Agricultural Equipment.

Research Direction	Technical Characteristics	Advantages	Application Scenarios
Smart Contract Automated Verification Protocol [69]	Achieves end-to-end automated pairing through programmable logic	Significantly improves cross-brand device integration efficiency	Multi-brand equipment collaboration
Lightweight Cryptography with Blockchain Hybrid Architecture [70]	Combines ECC encryption with hash anchoring technology	Maintains high performance in resource-constrained environments	Low-power agricultural IoT environments
ZKP Scheme [71]	Verifies identity without information disclosure based on cryptographic principles	Effectively protects business confidentiality and privacy	Agricultural machinery rental market/sensitive data exchange
Multimodal Biometric Authentication [36]	Enhances security through physical-layer biometrics (e.g., device fingerprints)	Provides physical tamper-proof protection with excellent anti-theft performance	Field equipment security protection

These techniques operate in a layered security hierarchy: biometric identification confirms physical device ownership; ZKPs ensure cryptographic identity privacy; smart contracts automate verification logic; and the blockchain provides immutable audit trails.

The security of the IoT in agriculture needs to balance the intensity of protection and the implementation cost. Core data can be encrypted with ZKP advanced encryption, and lightweight solutions can be used for regular communication. Protocol optimization and hardware acceleration can enhance verification efficiency, making solutions like ZKP more practical. The actual deployment should be handled in a hierarchical manner: basic encryption should meet the requirements of self-owned equipment, ZKP is suitable for high-value leasing scenarios, and a hybrid architecture is applicable to data exchange. The optimal solution is a layered design: biometric authentication at the device layer, efficient encryption at the network layer, and enabling advanced protocols for critical services, ensuring security while controlling costs.

Current research trends aim to evolve interoperability frameworks in three dimensions: from point-in-time certification to full life-cycle trust management, from generic solutions to agriculture-specific protocols, and from technical validation to integration with operational business models.

5. Stage-Specific Cognitive Applications

Having established the technical underpinnings for autonomous agricultural machinery, this section illustrates how these capabilities translate into practice across three key field scenarios: field preparation synergy, plant protection coordination, and harvest logistics orchestration. These stage-specific applications exemplify the critical transition of smart agriculture from controlled environments to real-world deployment.

5.1. Field Preparation Synergy

Field preparation synergy operates at both the operational planning and real-time execution levels. At the planning level, utilizing the hybrid particle swarm optimization and neighborhood strategy search (HPSO-NS) method that considers sequence-dependent setup times to generate optimal task sequences and schedules for agricultural machinery, which outperforms the traditional particle swarm optimization (PSO) by 7.328% in solution quality [73]. This method incorporates the conversion time of agricultural machinery into the optimization objective by establishing a six-stage tillage model (pre-land preparation, soil loosening, ploughing, post-land preparation, land leveling, and fertilization). Its output is a high-level work plan that dictates the order and timing of machinery operations, making it particularly suitable for compound farmlands that require multiple tillage and preparation operations. At the execution level, for hilly areas with complex terrain, an adaptive ploughing depth control method based on engine load characteristics produces real-time actuator commands [31]. This system dynamically adjusts the ploughing depth by real-time monitoring of engine speed, throttle opening, and hydraulic pressure. The final output is a control signal to the hydraulic system, ensuring that the tractor always operates within the optimal power range during the execution of the planned tasks.

The innovative design of the combined tillage machine [74] has achieved a technological breakthrough in completing seedbed preparation in a single operation. This technology integrates the rotary tiller, disc rake and flat soil plate into the same frame. Through the hydraulic system, the working components can be quickly switched, effectively solving the negative traction problem caused by traditional combined machinery. It is suitable for plain areas with relatively uniform soil conditions. Meanwhile, based on the fuzzy analytic hierarchy process and the improved grey wolf optimizer (IGWO) [75], a multi-objective optimization method is adopted, which is 20.33% higher than the traditional grey wolf optimizer (GWO). By constructing an integrated multi-sensor tillage depth monitoring system and a cutter roller speed adjustment system, precise control over the forward speed, tillage depth and cutter roller speed is achieved.

5.2. Plant Protection Operation Coordination

Sowing machinery and tractors have formed a highly integrated operation system in modern agriculture. The tractor provides traction power and electrical support for the seeder, and at the same time achieves mechanical connection through a three-point suspension system [76]. Modern intelligent seeders use motor-driven seed placement devices and, in combination with tractor navigation systems, achieve precise sowing, solving the problem of missed sowing in traditional mechanical transmission.

The vision-based precision weeding system has established a complete closed-loop control process, integrating perception, decision-making, and execution [77]. This system acquires field images through the visual sensors carried by the tractor, and uses the YOLOv3 algorithm to identify and locate weeds. After the visual system identifies the position of the weeds in real time, it transmits control instructions to the actuator. At the same time, the action timing of the weeding tool is precisely matched with the traveling speed of the tractor to ensure that the weeding operation is completed at the correct position. The system adopts a modular design, which is convenient for installation and adjustment, and can adapt to different operation requirements. At the same time, power transmission and real-time obstacle avoidance control [78] can be achieved through three-point suspension connection. The coordinated optimization of the obstacle detection signal acquisition device and the tractor’s traveling speed significantly improves the weeding coverage rate.

The joint operation parameter optimization system [79] realizes the integrated operation of “sowing − fertilizing − weeding” through the synergy of the vertical active straw removal device and the spray system. The power output shaft of the tractor simultaneously drives the straw removal device and the spray system. Through the optimization of the mechanical transmission ratio, the synchronous operation of each component is ensured. The practical value of this research lies in solving the problem of easy clogging of traditional plant protection machinery under straw covering conditions, and achieving the integration of agricultural machinery and agronomy through institutional innovation.

5.3. Harvest Logistics Coordination

Following field preparation and plant protection, the focus shifts to the final, time-critical stage of the agricultural chain: harvest logistics coordination.

The integration of sliding mode control (SMC) with radial basis function (RBF) neural network techniques offers an effective approach to addressing model uncertainty and external disturbances in harvest collaboration systems [80]. This methodology begins by constructing a dynamic model of the tractor-trailer system that incorporates nonholonomic constraints, describing the system behavior through second-order Euler-Lagrange error dynamics. The RBF neural network is employed to estimate and compensate in real time for uncertainties arising from factors such as soil resistance and load variations, while the sliding mode control mechanism ensures system robustness in the presence of estimation errors. This approach is particularly well-suited for addressing common agricultural challenges such as uneven terrain and time-varying loads. Its engineering significance lies in enabling stable control performance without reliance on precise prior modeling information.

Furthermore, the PID control strategy that integrates prescribed performance control (PPC) with neural networks provides guaranteed performance bounds for harvest synergy systems [81]. This method employs nonlinear transformation to convert constrained relative pose errors into unconstrained variables and utilizes a multi-layer feedforward neural network to approximate system uncertainties. The incorporation of PPC techniques allows explicit constraints on error convergence rate and overshoot through a defined performance function, ensuring that the inter-machine distance remains within a predefined safety range. The academic contribution of this method stems from its pioneering application of PPC to the collaborative control of agricultural machinery, effectively addressing the limitation of traditional approaches in simultaneously ensuring transient performance and steady-state accuracy.

Considering the practical issue of unreliable velocity sensors in field operations, an output feedback control scheme based on a saturation function is proposed. This method introduces a state observer to estimate unmeasurable velocity signals and employs a generalized saturation function to constrain both the control input and observer state amplitudes. An adaptive law based on the projection operator ensures boundedness of neural network weights, thereby preventing parameter drift. The comparative simulation shows that this scheme reduces the root mean square value of the distance tracking error from 13.26 m to 7.73 m, an increase of more than 40% [82]. The innovation of this approach lies in its effective resolution of common sensor limitations encountered in agricultural machinery, with strict constraints on control signal amplitude that align with real-world actuator capabilities, thereby enhancing engineering applicability.

6. Enabling Technology Ecosystem

Our complex cognitive applications across various operational phases rely on a robust ecosystem of enabling technologies. This ecosystem, comprised of key hardware and software modules, enables autonomous agricultural machinery to effectively perceive, reason, and act in dynamic field environments. To achieve reliable and scalable collaborative control, this ecosystem must adhere to core design principles and cultivate advanced technological capabilities to ensure interoperability, flexibility, and adaptability across devices and scenarios. The coordinated combination of multiple enabling technologies, including neuromorphic computing for distributed intelligence and FarmOS for system integration, forms the integrated foundation necessary to achieve advanced collaborative control in precision agriculture. As shown in Figure 5, we propose an integrated cooperative control ecosystem combining neuromorphic computing and FarmOS to overcome real-time coordination bottlenecks, a concept central to this integrated foundation.

Figure 5. Integrated technology ecosystem enabling cooperative tractor-implement control.

Specifically, this technology ecosystem supports collaborative control through a multi-layered converged architecture. At the perception layer, multimodal sensing technologies (such as LiDAR, vision, and GNSS-IMU fusion) provide environmental awareness and positioning capabilities for tractors and implements. Tractor-specific sensing focuses on navigation and terrain processing, while implement-specific sensing monitors work quality and status. These two enable data sharing and fusion through collaborative sensing (such as UWB-based relative pose estimation and implement obstacle detection), providing a unified scene understanding for real-time decision-making. At the decision-making layer, agricultural neuromorphic computing, through an event-driven spiking neural network architecture, enables distributed real-time decision-making with microsecond latency and milliwatt power consumption. This effectively addresses the performance bottlenecks of traditional computing architectures in resource-constrained edge environments and provides an efficient computing paradigm for multi-machine collaborative path planning and dynamic task allocation. At the integration layer, FarmOS, an integrated middleware framework, solves the interoperability challenges between heterogeneous devices through a hardware abstraction layer, a unified data model, and an API gateway, enabling plug-and-play, dynamic identification, and collaborative task planning, scheduling, and execution for cross-brand agricultural machinery fleets. These technology modules operate not in isolation but are tightly integrated to form a synergistic whole. For example, event stream perception data processed by the neuromorphic computing unit can be connected to the system’s decision loop via FarmOS’s standardized interfaces, while device status information managed by FarmOS can in turn optimize the neural network’s collaborative control strategy [83]. This deep integration enables the entire ecosystem to support complex application scenarios such as dynamically adjusting plow depth based on real-time soil resistance and coordinating grain unloading during multi-machine collaborative harvesting, significantly improving the efficiency, precision, and sustainability of agricultural operations.

In short, through technological innovation and deep integration at the three levels of perception, decision-making, and integration, this technology ecosystem has built an infrastructure capable of supporting highly autonomous, adaptive, and scalable cooperative control of agricultural machinery and implements, providing a comprehensive solution to the real-time, energy-efficiency, and interoperability challenges of precision agriculture.

6.1. Perception Technologies for Cooperative Control

The foundation of effective collaborative control lies in a multimodal perception system capable of generating a dynamic and highly realistic model of the environment. This integrated perception capability is crucial for achieving real-time coordination between autonomous tractors and implements, moving beyond independent operation to true collaborative collaboration. The perception architecture must encompass three complementary areas: tractor-centric navigation, implement-centric task monitoring, and crucially, collaborative perception. Collaborative perception bridges these two systems, overcoming coordination bottlenecks.

Tractor-centric perception provides global context for navigation and terrain control. Technologies such as GNSS-inertial measurement unit fusion and real-time leveling are crucial for maintaining operational stability and accuracy, especially in complex terrain. Otherwise, significant ground slopes can severely impact accuracy [84]. These systems enable the tractor to determine its precise position and orientation, establishing a base coordinate system for all subsequent collaborative operations. Forward-looking sensors such as LiDAR and stereo vision further enhance situational awareness, enabling advanced terrain mapping and obstacle detection, allowing the entire system to anticipate and respond to upcoming challenges.

Implement-centric perception focuses on monitoring the interaction between tools and their environment, as well as their operational status, providing critical feedback for task execution. This includes spectral perception technology, which combines hyperspectral imaging with neural network methods to eliminate response differences between equipment and establish unified quality assessment standards for equipment clusters [85,86]. Meanwhile, vibration and current sensors integrated with multi-state recognition models enable real-time monitoring of the health and load conditions of implements, supporting adaptive control strategies that significantly improve operational reliability [87]. These capabilities ensure that implements not only perform their primary functions but also communicate their status and requirements to the broader system.

The most critical advancement comes from collaborative perception technology, which establishes a continuous dialogue between tractors and implements. This includes technologies such as ultra-wideband (UWB) positioning for relative pose estimation, enabling precise spatial relationship tracking [88]; traction force sensing for adaptive power management [89]; and implement-based obstacle detection, providing ground visibility not available to tractors. By fusing these data streams into a unified situational awareness model, the system enables the responsive and adaptive coordination required for intelligent field operations, effectively overcoming the challenges that limit real-time coordination in traditional autonomous systems.

The comprehensive analysis of perception technologies highlights the diverse sensing modalities required for effective cooperative control. However, the selection of appropriate sensors involves careful consideration of their respective strengths, limitations, and suitability for specific agricultural applications. To systematically evaluate these trade-offs and provide clear guidance for sensor selection in autonomous agricultural systems, Table 4 presents a detailed comparative analysis of the primary perception technologies, focusing particularly on the complementary roles of cameras and LiDAR in addressing the unique challenges of agricultural environments. This comparison, supported by literature-based usage frequency data, illuminates the technical rationale behind sensor selection strategies for achieving robust perception in cooperative tractor-implement operations.

Table 4. Comparative Analysis of Perception Sensors for Agricultural Cooperative Control.

Characteristic	Camera	LiDAR
Data Modality	2D RGB/Multispectral/Hyperspectral images	3D point cloud with intensity information
Information Type	Rich texture, color, semantic information for crop/weed identification [85]	Precise geometry, shape, and direct distance measurements
Ranging Capability	Indirect (requires stereo vision or motion parallax computation)	Direct, high-precision (±2–5 cm) ranging [88]
Lighting Dependency	High (performance degrades at night, dawn/dusk, strong shadows)	Low (active illumination enables 24/7 operation)
Weather Robustness	Sensitive to rain, snow, fog, and dust particles	Performance degradation in heavy fog, rain, and dust storms
Computational Demand	High for neural network-based processing [90]	Moderate for point cloud processing and obstacle detection
Primary Applications	Crop row navigation, species identification, work-quality assessment, plant health monitoring	Obstacle detection, terrain mapping, relative localization, implement pose estimation [91]
Cost Range	Low to medium ($100–$2000)	Medium to high ($2000–$20,000+)
Cooperative Role	Implement-based obstacle detection, quality monitoring for implement operations	UWB-enhanced relative pose estimation, shared situational awareness [92]
Literature Usage Frequency	High (70–80%) in vision-based navigation and monitoring	Medium-High (50–60%) in safety-critical and precision applications

6.2. Agricultural Neuromorphic Computing for Cooperative Control

Neuromorphic computing, inspired by the structure and function of the biological brain, offers a transformative paradigm for achieving efficient, real-time cooperative control between tractors and farm implements. In complex agricultural environments, the dynamic coordination required for operations such as plowing, seeding, and harvesting demands ultra-low latency and extremely low energy consumption to ensure synchronization and responsiveness. Traditional computing architectures often struggle to meet these requirements in resource-constrained edge environments. In contrast, neuromorphic hardware platforms (e.g., Intel Loihi, SpiNNaker) employ event-driven principles, achieving microsecond response times and milliwatt power consumption. This is crucial for distributed, real-time decision-making and clarifies that these are not simple microcontroller solutions but specialized hardware, often deployed at the edge alongside or integrated into more powerful processing units to handle complex SNN models. As demonstrated in MAS, spiking neural networks (SNNs) have reduced response times by 50% and lowered energy consumption per machine per day by approximately 3.6 kWh compared to traditional controllers, significantly extending the operational life of field applications [93,94].

The application of neuromorphic computing to agricultural collaboration involves encoding multimodal sensory data (such as LiDAR scans, visual input, equipment status, and terrain feedback) into temporal spike trains. This data is processed using a distributed spiking neural network (SNN) architecture equipped with biologically plausible learning rules such as spike-time-dependent plasticity (STDP). This approach excels in generating coordinated control signals for multi-machine systems, even in complex scenarios requiring synchronized path planning or dynamic task allocation. It maintains strong real-time performance, with response latency below 10 ms and peak power consumption below 5 watts, even in unpredictable outdoor conditions, significantly outperforming traditional centralized controllers [94]. Furthermore, the neuromorphic vision system, which converts visual features into spikes, demonstrates enhanced environmental robustness in the shared perceptual space of the device group, reliably supporting machine identification, obstacle detection [95], and real-time crop status classification [90], which are critical for situational awareness in collaborative tasks.

Empirical studies of neuromorphic multi-robot systems demonstrate their potential for cooperation: the integration of event-driven spiking neural networks (SNNs) with dynamic vision sensors creates an efficient, low-power perception-response loop, which is critical for maintaining formation and preventing collisions between machines operating in close proximity [96]. Neuromorphic hardware platforms such as SpiNNaker and Intel Loihi enable the deployment of distributed intelligent agents within a single machine with milliwatt power budgets, facilitating the seamless integration of local processing with global cooperative goals. This architecture significantly improves performance in dynamic, heterogeneous perception environments [97], effectively overcoming the energy and latency barriers that have traditionally limited scalable distributed agricultural systems [98]. For example, SNNs can adjust implement depth in real time based on soil resistance peaks, or allow a tractor to dynamically recalibrate its path after receiving event-based cues from a subsequent planter [99].

Current research is advancing along several key directions: developing lightweight SNN deployment strategies, creating online frameworks for learning cooperative policies, and exploring novel neuromorphic perception paradigms. These innovations are steadily advancing the practical deployment of this bio-inspired technology in field systems. The ultimate goal is to tightly integrate neuromorphic processing with farm management systems (such as FarmOS), enabling low level, event-based perception and control decisions to be seamlessly integrated into high level operational planning and monitoring. This integration has the potential to become the core of a highly responsive, robust, and energy-efficient autonomous and cooperative ecosystem for modern agriculture.

6.3. FarmOS Integration Framework for Cooperative Control

In the modern agricultural ecosystem, effective cooperative control between autonomous tractors and their supporting agricultural implements faces fundamental interoperability challenges. Due to protocol fragmentation, operational clusters comprised of smart tractors from different brands (with heterogeneous control interfaces), diverse sensor systems, and supporting agricultural implements using proprietary protocols often form severe “information silos”, hindering real-time data exchange and system interoperability [100]. This fragmented state severely limits collaborative decision-making, particularly for complex collaborative tasks requiring precise timing synchronization, such as multi-machine coordinated precision seeding [101]. The FarmOS system, by building a standardized, open framework, provides a targeted solution for seamless coordinated control of heterogeneous agricultural equipment. Its high compatibility of 95% with tested equipment is demonstrated through successful integration with major agricultural machinery manufacturers such as John Deere (Moline, IL, USA), CNH Industrial (Case IH, Racine, WI, USA; New Holland, PA, USA), AGCO (Fendt, Marktoberdorf, Germany; Massey Ferguson, Duluth, GA, USA), and CLAAS (Harsewinkel, Germany), as well as numerous providers of implements and sensors [92,102].

As a core middleware platform, FarmOS plays a key role in machine-to-machine (M2M) communication and task execution coordination. Its architecture eliminates hardware differences through a unified interface, enabling autonomous tractors to dynamically identify, configure, and synchronize with various agricultural implements, whether plows, seeders, or harvesters, breaking the limitations of manufacturer-specific protocols. This capability lays the foundation for AI-based collaborative behaviors, such as coordinated path tracking, equipment status monitoring, and adaptive task planning, dynamic scheduling, and real-time execution control among fleets of machines [84]. Quantitative evaluations demonstrate the practical impact of FarmOS: in mixed-brand fleets, it reduces inter-machine command latency to under 50 ms (compared to over 200 ms for traditional systems), directly improving synchronization accuracy during collaborative operations. Such as sequential harvesting, which requires high coordination between machines [92]. Furthermore, compatibility testing demonstrates that FarmOS achieves 95% cross-brand device recognition and control interoperability, a success rate far exceeding the 65% found in non-integrated systems. This ensures reliable matching of tractors and their corresponding implements, which is crucial for achieving collaborative functionality [102]. This includes not only high-level task scheduling in operational calendars but also real-time autonomous task planning and execution in the field, such as dynamic route optimization and implement control sequences.

FarmOS’s architectural design utilizes a layered framework designed to optimize collaborative machine control applications. Its hardware abstraction layer (HAL) effectively decouples control logic from underlying proprietary implementation protocols by providing a unified driver interface, laying the foundation for key plug-and-play functionality. The data bus and message middleware layer, built on a publish-subscribe mechanism, serves as the hub for high-throughput information exchange during collaborative operations. This ensures that critical control commands between tractors and implements during synchronized field operations are transmitted within 100 ms, essential for maintaining formation accuracy and executing coordinated actions. Furthermore, the unified data model and ontology layer establish standardized semantic representations for agricultural entities (such as machine capabilities, mission parameters, and field status), ensuring consistent information understanding across different devices. This semantic interoperability enables tractors to intelligently coordinate operations with implements within a shared working environment. For example, dynamically adjusting speed and force distribution when pulling multiple implements simultaneously. Finally, the API gateway layer facilitates the bidirectional flow of collaborative control signals between the fleet management system and field machinery, effectively coordinating the relationship between local autonomous decision-making and global collaborative strategies [103].

This integrated architecture expands FarmOS’s capabilities from basic equipment management to key aspects supporting collaborative agricultural operations. Its development practice reflects a shift toward an open architecture paradigm, aiming to overcome the limitations of proprietary technologies and directly address the real-time collaborative operations of autonomous tractors and agricultural machinery fleets. As a unified digital ecosystem, FarmOS provides the critical middleware infrastructure that integrates heterogeneous agricultural machinery into a collaborative intelligent swarm capable of synchronized operation and adapting to field conditions. For example, by defining a unified agricultural implement control interface, FarmOS enables tractors to directly adjust the seeding rate of a planter or the flow rate of a fertilizer spreader, achieving closed-loop control based on real-time environmental feedback [104]. Furthermore, its task coordinator can dynamically allocate collaborative tasks (such as grain unloading coordination in multi-machine cooperative harvesting) based on the capabilities and real-time status of agricultural machines, thereby improving overall operational [105]. This integrated architecture expands the functions of FarmOS from basic device management to comprehensive task planning, scheduling and execution control for collaborative agricultural operations. At the same time, the system supports both strategic task scheduling based on schedules and tactical autonomous task planning and execution in dynamic outdoor environments.

6.4. Real-Time Operating Systems for Agricultural Machinery

While middleware platforms like FarmOS are indispensable for achieving high-level interoperability and fleet-wide data orchestration, the execution of safety-critical and time-sensitive control loops demands the deterministic performance of a real-time operating system (RTOS) at the hardware level of individual machinery. The inherently unpredictable nature of agricultural environments, with sudden obstacles, varying soil conditions, and the need for precise implement control, necessitates a computing environment where task completion times are guaranteed within strict deadlines. An RTOS provides this determinism through prioritized, preemptive task scheduling, ensuring that high-priority functions, such as processing a LiDAR scan for immediate collision avoidance or executing the closed-loop control algorithm for steering, are never delayed by lower-priority background tasks. This capability is crucial for functional safety, as a delayed response in actuating brakes or lifting an implement can lead to catastrophic outcomes. Research in autonomous agricultural vehicles underscores the necessity of real-time scheduling to manage concurrent perception, planning, and control tasks reliably, a requirement that general-purpose operating systems cannot meet due to their non-deterministic latency and scheduling jitter [106].

The relationship between FarmOS and an RTOS is complementary, operating at different layers of the software stack to collectively address both interoperability and real-time execution challenges. FarmOS acts as the integration middleware that facilitates communication and data flow between different machines and with cloud-based farm management systems. Underneath this, on each individual tractor’s or implement’s electronic control unit (ECU), a lightweight RTOS such as FreeRTOS or QNX manages the low-level, time-critical processing. For instance, while FarmOS may issue a high-level command to “begin seeding operation”, the RTOS on the seeder’s ECU is responsible for the deterministic control loop that reads encoder pulses, adjusts seed metering motor speed, and monitors blockages, all within millisecond-level timing constraints. This architectural pattern, separating high-level coordination from low-level deterministic control, is a established best practice in complex cyber-physical systems. Studies on robotic operating systems (ROS) in agriculture highlight the trend of integrating high-level frameworks with real-time components, where ROS handles perception and decision-making while a co-designed RTOS manages actuator control for guaranteed performance [107]. Therefore, the integration of an RTOS is not a replacement for FarmOS but a foundational enhancement that ensures the time-sensitive commands and data exchanged through FarmOS can be executed upon reliably and safely at the machine level.

6.5. Communication Technologies for Agricultural Environments

The efficacy of a cooperative control system is fundamentally constrained by the reliability, latency, and coverage of its underlying communication network. Agricultural environments present a unique set of challenges for data exchange, including vast operational areas, variable topography that causes signal occlusion, and the presence of electromagnetic interference from powerful machinery. No single communication technology provides an optimal solution for all scenarios; instead, a heterogeneous network architecture that strategically leverages the strengths of various technologies is required to meet the diverse needs of agricultural operations. Short-range technologies such as Wi-Fi and Bluetooth are suitable for farmyard setup and data offloading due to their high data rates, but their limited range and power consumption render them impractical for in-field operational communication. In contrast, low-power wide-area network (LPWAN) technologies like LoRa (long range) excel in transmitting sporadic, low-data-rate sensor readings from soil moisture probes or weather stations over several kilometers, but their low bandwidth and relatively high latency make them unsuitable for the high-frequency control commands required for real-time machine coordination [108].

For the core data links enabling fleet management and wide-area coordination, medium to long-range wireless technologies are paramount. Cellular networks, particularly 4G LTE and the emerging 5G, offer a compelling balance of coverage, bandwidth, and latency. 4G provides robust connectivity for telemetry, video streaming, and most fleet management tasks. The advent of 5G is poised to be a transformative force, promising not only enhanced mobile broadband (eMBB) for high-data-rate applications but, more critically, ultra-reliable low-latency communication (URLLC) for safety-critical control signals and massive machine-type communication (mMTC) for connecting vast arrays of sensors. However, the viability of cellular networks is contingent upon reliable signal coverage, which remains inconsistent in many remote rural agricultural regions, creating a significant connectivity gap [109]. To bridge this gap, satellite communication provides a global coverage solution. While traditional geostationary (GEO) satellites have been available, their high latency, often exceeding 500 ms, is prohibitive for real-time control. The recent deployment of low-earth orbit (LEO) satellite constellations, such as Starlink, represents a revolutionary advancement. By operating in orbits much closer to earth, LEO satellites drastically reduce latency to levels comparable to terrestrial networks (20–40 ms) while offering high bandwidth, thereby enabling real-time fleet coordination, live video feeds for remote oversight, and seamless cloud connectivity for machinery operating in the most remote fields without reliance on ground-based infrastructure [110]. In practice, a cooperative agricultural system will therefore employ a multi-modal communication strategy, potentially using a direct UWB link for critical tractor-implement control, a local 5G network for intra-fleet coordination, and a Starlink backbone for robust, high-bandwidth connectivity to the cloud and farm management platform, ensuring operational resilience across the varied landscapes of modern agriculture.

6.6. Design Principles and Core Capabilities

The development of AI-driven collaborative agricultural machinery systems does not rely on a single technological breakthrough, but rather on a comprehensive and continuously evolving technological ecosystem. The core of this system is established by three fundamental design principles with inherent progressive and constraining relationships: heterogeneous integration forms the physical foundation for collaborative operations; modular design, built upon heterogeneous integration, provides flexibility and scalability; and safety redundancy mechanisms ensure reliable operation in complex environments, guaranteeing safe autonomous operation.

First, heterogeneous system integration is the key foundation for building a closed-loop “perception-decision-execution” collaborative system. It requires deep integration and seamless connection of subsystems such as the mechanical system, control system, multi-source sensing, and AI algorithms. Specifically, raw data from sensors (e.g., field environment information) must be transformed into effective operational actions through tightly coupled sensor fusion technology, advanced control algorithms, and precise actuator commands. For example, the combine harvester header edge detection system based on stereo vision technology demonstrates the high integration between multi-modal perception and real-time decision-making and execution [91,111].

Second, modular and standardized interface design, built upon heterogeneous integration, significantly enhances system flexibility and maintainability. By using unified mechanical and digital connection interfaces, different types of agricultural machinery (such as strawberry transplanters with high precision requirements for operation depth control) can achieve plug-and-play compatibility. This design allows the system to automatically identify and configure new equipment, greatly reducing the complexity of system expansion and upgrades [112].

Finally, a safety-first redundancy mechanism, built upon heterogeneous integration and modular architecture, ensures system-level reliability for autonomous field operations. To address complex agricultural environments and potential risks such as hardware failures and communication interruptions, multi-level fault-tolerant design is essential. For example, using a dual-bus control architecture for communication redundancy or deploying redundant actuators are effective strategies to ensure that the system can maintain basic safety operations even if some subsystems fail. This safety-oriented design philosophy plays an irreplaceable role in ensuring the successful completion of autonomous operation tasks [113]. Table 5 summarizes the aforementioned design principles, their technical implications, and specific implementation strategies. Overall, these principles collectively support the development of key technologies such as environmental sensing systems, neuromorphic computing architectures, and RTOS frameworks, thus laying a solid theoretical foundation for building efficient, reliable, and intelligent agricultural machinery control systems.

Table 5. Design Principles for Autonomous Agricultural Machinery Cooperative Control Systems.

Design Principle	Technical Connotation	Key Capability Requirements	Technical Implementation Path
Heterogeneous System Integration	Deep integration of mechanical, electronic, information and AI technologies to build a “perception-decision-execution” closed loop	(1) Cross-domain integration (2) Data-driven control (3) End-to-end response	New energy intelligent tractor platform Integrated digital twin model Multi-modal sensor fusion
Modular Design	Standardized interfaces supporting plug-and-play connectivity and parameter self-configuration for diverse implements	(1) Interface compatibility (2) Dynamic recognition (3) Adaptive reconfiguration	Plug-and-play stack (ISOBUS) Self-configuration knowledge base
Safety Priority	Multiple redundancy designs ensuring operational safety during unmanned tasks	(1) Fault diagnosis (2) Redundancy switching (3) Safety boundary maintenance	Dual-bus control architecture Multi-sensor cross-validation Real-time safety monitoring

7. Research Challenges and Emerging Paradigms

A technological ecosystem that supports autonomous agriculture is bound to lead to a complex interdependent relationship between tractors and farm implements. These symbiotic relationships face some fundamental challenges and require collaborative solutions in areas such as perception, planning, control, and safety. To systematically illustrate the relationship between core research challenges and emerging AI paradigms, Table 6 comprehensively summarizes the key challenges in tractor-implement collaborative operations, corresponding AI-driven solutions, and their expected benefits, across the four dimensions of perception, planning, control, and safety. This table clearly demonstrates how cutting-edge collaborative paradigms can address these technical challenges and provides a preview of their practical field applications.

Table 6. Core Cooperative Challenges and Emerging AI Paradigms.

Challenge Type	Research Challenge	Emerging AI-Driven Paradigm	Expected Field-Level Benefit
Perception Challenges	Spectral ambiguity in crop-weed discrimination under variable lighting.	Adversarially Robust & Explainable Cooperative Perception	Enhanced classification accuracy in deceptive conditions [114]
	Mechanical vibrations distorting grain flow sensor signals.		Improved signal integrity and operator trust [115]
Planning Challenges	Kinematic incompatibility in heterogeneous fleets.	Swarm Intelligence for Self-Organizing Cooperation	Dynamic task reallocation and fault-tolerant control [116]
	Dynamic obstacle avoidance in noisy field conditions.		Millimeter-precision cooperative maneuvering [117]
Control Challenges	Soil tillage variations reducing sowing uniformity.	Biomechanically-Informed Cooperative Mechatronics	Improved seeding uniformity and reduced compaction [118]
	Spray penetration issues in dense canopies.		30% improvement in droplet coverage [119]
Safety Challenges	Implement instability on uneven terrain.	Physics-AI Hybrid Digital Twins	Proactive risk prediction and validation [120]
	Coordination failures in multi-implement scenarios.		Virtual testing of edge-case scenarios [121]

7.1. Core Research Challenges

7.1.1. Kinematic-Aware Cooperative Planning for Heterogeneous Fleets

The maneuverability mismatch between tractors and implements creates fundamental coordination barriers. Articulated harvesters exhibit turning radii incompatible with compact orchard tractors, while trailed implements introduce nonholonomic constraints absent in autonomous tractors. This kinematic diversity prevents unified trajectory representation, particularly during simultaneous ground operations where differential mobility causes spatial conflicts. Communication limitations fracture coordination integrity: signal attenuation between tractors and balers during corn harvesting causes desynchronization in material transfer, while dynamic payload shifts in grain carts invalidate precomputed paths [122]. These constraints highlight the inadequacy of conventional multi-agent frameworks in reconciling physical dissimilarities with millimeter-level precision requirements for implement coupling.

Biological time-pressure compounds planning complexity during cooperative tasks. Perishable crop harvesting requires real-time replanning when primary harvesters malfunction, yet current systems lack dynamic task reallocation protocols [123]. Intra-row weeding requires subsecond negotiation when unexpected obstacles appear, while pollinator coordination fails to adapt routes to wind-induced canopy movements [124]. Reinforcement learning approaches [125] remain computationally prohibitive for such time-sensitive operations, exposing a critical need for lightweight contingency management in tractor-implement collectives.

7.1.2. Robust Perception and State Estimation for Implements Coordination

One of the key challenges faced by agricultural perception is spectral ambiguity, which poses a major obstacle to the interaction between agricultural machinery and target crops. Under varying lighting conditions, over 40% of the spectra of crops and weeds overlap, leading to misclassification of visual-guided weeding machines; at the same time, soil moisture gradients can alter the terrain reflection characteristics and affect the judgment of the tillage depth sensor [126]. In GNSS-denied environments such as orchards, these problems are even more prominent: signal shadows caused by canopy shading can interfere with the relative positioning between tractors and agricultural machinery [127]. Particularly crucially, the interference caused by the machinery itself can contaminate the sensing data: the fluid in the spray tank causes fluid dynamic noise in the inertial unit [128], and the vibration generated by the interaction between the machinery and the soil masks the true kinematic state during the farming process [115]. These phenomena collectively reveal a significant deficiency in the current sensor fusion architecture: in collaborative operations, existing methods are still unable to effectively decouple the dynamic characteristics of agricultural machinery from environmental noise.

7.1.3. Disturbance-Rejection Control for Implements Synchronization

Agricultural control is confronted with the problem of “implementation of terrain interference propagation”, which can undermine the coordination accuracy. The soil rheological gradient can lead to inconsistency in the wheel sliding between the tractor and the towing equipment [118], thereby causing deviations in the sowing operation. Variable draft forces from soil compaction disrupt depth synchronization between multi-section tillage tools. Sprayer boom oscillations induced by tractor motion create deposition inhomogeneity exceeding 30% [119]. These disturbances cascade through control chains: tank sloshing generates coupled roll-yaw oscillations during liquid application [128], while baler feeder vibrations corrupt force feedback during tractor-implement power transfer [129].

The bio-mechanical interface introduces unique control challenges for implement end-effectors. Fruit detachment mechanics require adaptive force modulation during robotic harvesting [130], while seed metering systems demand real-time calibration as seed coatings hydrate [131]. Crucially, current controllers treat implements as isolated mechanical systems rather than dynamic extensions of tractors. Torque distribution strategies [132] ignore implement-crop interaction forces, while predictive controllers [123] lack biological state feedback for closed-loop adjustment during cooperative tasks.

7.1.4. Safety Assurance for Close-Proximity Cooperation

The autonomous tractor-hitching device team is confronted with the problem of amplified collision risks due to unmodeled interaction dynamics. The opaque decision-making mechanism of the visual servo system [133] makes it difficult to accurately predict the swinging trajectory of the hitching device during sharp turns, while the neural sensor [134] fails to provide effective diagnostic information when the grain flow suddenly increases and affects the relative positioning. Mechanical systems typically lack embedded fault prediction capabilities, for example, when the baler feeding mechanism is blocked without warning due to material overload [135], the towing rope may be subjected to dangerous tension. These risks peak during collaborative operations: when communication delays are compounded with hydraulic response delays, the header of the combine harvester unit may collide.

Existing verification methods have significant issues, namely, insufficient coverage of collaborative edge-case scenarios. The current framework struggles to effectively reproduce multiple critical scenarios, such as folding accidents of agricultural machinery on slippery slopes after rain [136], resonance of power take-off shafts under high-load operations, or spray drift during the cooperative operation of tractors and drones in side-wind conditions [137]. Fatigue damage to the linkages of agricultural machinery usually only becomes apparent after hundreds of hours of operation, which is beyond the scope of accelerated testing. Most critically, there is currently no digital twin model capable of accurately simulating the dynamic interactions between tractors and agricultural machinery, which poses a fundamental obstacle to the safety certification of close-range autonomous collaborative systems.

7.2. Emerging Paradigms and Key Directions

7.2.1. Swarm Intelligence Empowers Scalable Self-Organizing Cluster Collaboration

Traditional cooperative control of agricultural machinery relies on centralized or hierarchical decision-making architectures, which face significant bottlenecks in scalability, flexibility, and robustness when coordinating heterogeneous swarms of equipment in dynamic agricultural environments. The emerging swarm intelligence paradigm offers a transformative approach, inspired by established robotics applications where distributed control has achieved remarkable success. In multi-robot systems such as RoboCup and autonomous drone swarms, complex collective behaviors (including dynamic formation control, coordinated transportation, and area coverage) arise from simple local interaction rules and inter-neighborhood communication protocols, eliminating the need for central coordination [138,139]. These robotic implementations provide a proven framework for decentralized coordination that is directly applicable to agricultural settings. Applying these principles to agricultural machinery enables swarms of equipment to self-organize through local perception and communication. For example, a swarm-based seeding system consisting of a master tractor and multiple autonomous seeders can dynamically adjust its operating mode using biomimetic algorithms such as ant colony optimization to achieve efficient field coverage while maintaining inherent fault tolerance. When a single unit fails, its tasks can be automatically redistributed across neighboring machines, significantly enhancing system resilience [116]. Key research directions include developing distributed consensus algorithms for unstructured farm environments, creating agent-based behavior prediction simulation platforms, and designing robust coordination strategies under communication constraints [117].

7.2.2. Adversarial Resilience and Explainability in Cooperative Perception

As agricultural machinery collaborative systems increasingly rely on multi-sensor fusion perception, they face not only traditional environmental noise and weather interference but also, more seriously, cutting-edge security threats targeting the perception systems themselves: adversarial attacks [114]. This makes adversarial robustness a key emerging paradigm for ensuring the safety of collaborative operations. In a collaborative unit consisting of agricultural machinery and implements, an attacker could inject imperceptible adversarial perturbations into the camera of an unmanned tractor, causing it to mistake a straw pile ahead for navigable flat ground. Alternatively, an attacker could use low-cost devices to transmit GPS spoofing signals, disrupting the high-precision relative positioning between master and slave agricultural machinery, causing the entire formation to deviate from its intended path or even collide. The formidable nature of these attacks lies in the fact that a single device’s perception vulnerability can disrupt an entire collaborative system that relies on shared perception information, triggering a cascading series of disastrous consequences [140]. Therefore, future research must go beyond traditional robustness and focus on building collaborative perception architectures with inherent attack resistance. For example, a consensus-based multi-machine cross-validation mechanism could be developed. When a machine’s perception results differ from those of the majority of members in the cluster, the system automatically flags it as a potential anomaly and initiates a review process. When master and slave agricultural machines collaborate to perform no-till seeding, the system should be able to clearly explain the sensory data (such as multispectral soil moisture images) and rules (such as agronomic knowledge graphs) used to generate coordinated instructions for adjusting seeding depth and spacing. Advancing this paradigm requires integrating the latest advances in adversarial machine learning, MAS security, and explainable AI.

7.2.3. Biomechanically-Informed Cooperative Mechatronics

A new generation of agricultural machinery systems incorporates bio-compliant concepts into cooperative mechanical control to optimize dynamic interactions with crops, soil, and the surrounding environment. This approach draws on biological mechanisms to enhance adaptability and reduce ecological impact. For example, in fruit picking, robot end-effectors employ tactile and force feedback systems to mimic the delicate grasping of the human hand, minimizing damage through compliant operation [141]. Similarly, terrain-adaptive mechanisms inspired by animal locomotion enable agricultural machinery to navigate delicate and uneven soils, reducing compaction and improving stability [142]. Artificial intelligence is a core enabler of these systems: deep learning models can accurately identify weeds and coordinate targeted spraying regimes, significantly reducing herbicide use while maintaining crop health [143]. Beyond plant-level interaction, computer vision and real-time motion data are used to synchronize the movements of agricultural machinery (such as planters and harvesters) with tractors, ensuring seamless collaboration across varying ground speeds and field conditions. In material handling, adaptive speed control and predictive planning facilitate efficient flow between coupled machines, such as combine harvesters and grain transporters, minimizing downtime and maximizing yield. Emerging research is also advancing human-robot collaboration, where operators can interact with swarms of intelligent machines through immersive interfaces, enabling responsive coordination based on real-time biological changes, such as crop density or pest infestations. Future developments may focus on biohybrid systems, where soft robotics is combined with AI-driven control to achieve unprecedented synergies between automation and agronomy.

7.2.4. Physical and AI Hybrid Digital Twins for Collaborative System Verification

Digital twins, a disruptive emerging paradigm, are expanding from industry to agriculture. They provide unprecedentedly powerful tools for the design, verification, and real-time optimization of agricultural machinery and implement coordination systems [121]. Going beyond traditional offline simulation, they aim to create a high-fidelity virtual representation of the actual machinery, implements, and environment in the physical field, synchronized in real time with data flowing bidirectionally. The core of this digital twin lies in its hybrid modeling approach, which deeply integrates first-principles physical models (such as the dynamics of soil-tire/implement interactions and the mechanical model of the tractor’s hydraulic suspension system) with artificial intelligence models that learn complex patterns from massive amounts of operational data (such as neural networks for predicting traction resistance under varying soil moisture conditions). This enables highly realistic simulation of the behavior of the entire coordinated system. In the context of agricultural machinery coordination, this means that master-slave coordinated navigation algorithms, traction force distribution strategies, and implement lifting synchronization logic can be tested and iteratively optimized billions of times in virtual space at extremely low cost and zero risk. This is particularly true for extreme operating conditions and failure scenarios that are difficult to replicate in the real world, such as the cascading impact of a planter jam on the entire formation’s operation.

A typical application is the development of a digital twin system for a planter fleet, which receives real-time operational data from a real fleet in a virtual environment and continuously predicts future states. Based on this, it can preview different collaborative path plans in advance and recommend an optimized path that minimizes overlaps and omissions while avoiding predicted wet and soft areas, which is then sent to the physical entity for execution, thereby achieving truly forward-looking collaborative decision-making [144]. In addition, this paradigm also opens up new avenues for the safety certification of collaborative systems. Regulators can require that a large number of test results of control algorithms in high-fidelity digital twins be used as an important basis for safety assessments. The key challenges in promoting the development of this paradigm lie in building high-precision agricultural environment and equipment models, achieving real-time synchronization and integration of massive heterogeneous data, and establishing an efficient data-model bidirectional drive mechanism [120].

8. Conclusions

This study demonstrates that reliable, efficient collaboration among agricultural machinery requires an integrated system coordinating operational physics, environmental dynamics, and intelligent decision-making, going beyond any single technology.

The knowledge-embedded AI framework, adaptive control strategy based on physical perception, and the collaborative ecosystem integrating neuro-architectural computing and FarmOS proposed in this paper have laid a systematic foundation for this field. These innovations not only significantly improve the collaborative accuracy and robustness of tractors and agricultural implements in unstructured environments, such as achieving dynamic matching of plowing depth and traction force through real-time soil resistance perception, or maintaining the synchronous stability of the sowing machine and the tractor in sloping terrain operations based on multi-modal perception, but also fundamentally promote agricultural collaborative control from single-machine automation to group embodied intelligence.

Looking to the future, we believe that more attention should be paid to the research on the force-movement-decision closed-loop collaboration intelligence between agricultural machinery and implements. For instance, in the scenario where combine harvesters and grain transport vehicles collaborate to unload grain, how to achieve millimeter-level relative positioning and adaptive control of torque; or when agricultural spraying robot groups collaborate in operations, by embedding biomechanical models to optimize the coupling response of spray rod actions and the movement speed of agricultural machinery. Digital twins and physical-AI hybrid simulation technologies will provide strong support for the testing and verification of these complex collaborative scenarios, enabling researchers to simulate various extreme working conditions in a virtual environment, significantly reducing the cost and risks of on-site testing. To systematically guide these advancements, Figure 6 visually outlines the integrated path from conceptual frameworks to real-world deployment, highlighting critical milestones in technology development and validation.

Figure 6. Key technical milestone for intelligent cooperative control technology of agricultural machinery.

Although there are still challenges in aspects such as perception robustness, compatibility of heterogeneous systems, and real-time collaborative architectures at present, we firmly believe that by continuously deepening the integration and innovation of agricultural physical knowledge with artificial intelligence, the autonomous agricultural machinery collaborative system will develop towards a more intelligent, reliable and practical direction. Future research should focus on the design of new machinery inspired by biomechanics, the optimization of agricultural-specific neuromorphic computing architectures, and the cross-platform expansion capabilities of the FarmOS ecosystem, ultimately achieving intelligent collaborative and global optimization throughout the entire agricultural production process. The research roadmap in Figure 7 provides a foundational guide to navigate these complexities, illustrating how incremental innovations can coalesce into a cohesive and scalable system for next-generation agricultural robotics.

Figure 7. Research roadmap for intelligent cooperative control technology of agricultural machinery.