Control Algorithms for Intelligent Agriculture: Applications, Challenges, and Future Directions

Abstract

Facing global pressures such as population growth, shrinking arable land, and climate change, intelligent agriculture has emerged as a critical pathway toward sustainable and efficient agricultural production. Control algorithms serve as the core enabler of this transition, finding applications in crop production, pest management, agricultural machinery, and resource optimization. This review systematically examines the performance and applications of both traditional (e.g., PID, fuzzy logic) and advanced control algorithms (e.g., neural networks, model predictive control, adaptive control, active disturbance rejection control, and sliding mode control) in agriculture. While traditional methods are valued for simplicity and robustness, advanced algorithms better handle nonlinearity, uncertainty, and multi-objective optimization, enhancing both precision and resource efficiency. However, challenges such as environmental heterogeneity, hardware limitations, data scarcity, real-time requirements, and multi-objective conflicts hinder widespread adoption. This review contributes a structured, critical synthesis of these algorithms, highlighting their comparative strengths and limitations, and identifies key research gaps that distinguish it from prior reviews. Future directions include lightweight algorithms, digital twins, multi-sensor integration, and edge computing, which together promise to enhance the scalability and sustainability of intelligent agricultural systems.

1. Introduction

1.1. Background and Motivation

Facing global pressures such as population growth, shrinking arable land, and climate change, intelligent agriculture has emerged as a critical pathway toward sustainable and efficient agricultural production. Control algorithms serve as the core enabler of this transition, finding applications in crop production, pest management, agricultural machinery, and resource optimization. This review systematically examines the performance and applications of both traditional and advanced control algorithms in agriculture.

While traditional methods remain valuable for their simplicity and robustness, advanced algorithms excel in handling nonlinearity, uncertainty, and multi-objective optimization, significantly improving precision and resource efficiency [1,2,3]. However, challenges such as environmental heterogeneity, hardware limitations, data scarcity, real-time requirements, and multi-objective conflicts hinder widespread adoption [4,5,6,7].

Intelligent seeding technology, as a core component of precision agriculture, has witnessed remarkable development. Liu et al. [4] indicate that by integrating advanced sensing technology with control algorithms, modern motor-driven seeders can increase seeding accuracy to over 95%. Similarly, in agricultural transportation, Chen et al. [6] developed an intelligent suspension system that reduces product loss by 30%. Cui et al. [7] further validated the value of intelligent suspension in spraying operations, improving pesticide distribution uniformity by 25%.

The complexity and variability of agricultural environments impose stringent requirements on control algorithms. Jin et al. [8] highlighted that threshold controllers only react to abnormalities, whereas GRU (Gated Recurrent Unit)-based predictive systems can preemptively adjust conditions, reducing energy waste by 90%. Lu et al. [9] demonstrated that combined MPC (Model Predictive Control) and SMC (Sliding Mode Control) with disturbance observers improve anti-interference capability by over 60% in tractor-trailer systems.

Climate change further intensifies these challenges. He et al. [10] revealed the dual impact of climate change on rice production through both direct and indirect pathways. Raheem et al. [11] emphasized soil management strategies like conservation agriculture to enhance climate resilience, while Iqbal et al. [12] underscored the role of control algorithms in optimizing resource use under climate uncertainty.

Model Predictive Control (MPC) has become a cornerstone in agricultural automation due to its ability to handle nonlinear and delayed systems. Ding et al. [13] detailed its applications in irrigation and greenhouse control, improving resource efficiency by over 30%. He et al. [14] achieved high path-tracking accuracy in paddy fields using MPC, and Ge et al. [15] maintained centimeter-level accuracy under variable conditions with adaptive sliding mode control.

Existing reviews on agricultural automation lack a comprehensive comparison of the performance, advantages, and limitations of traditional and advanced control algorithms across smart farming applications. Many existing reviews focus on a specific algorithm type (e.g., MPC or deep learning) or a single application scenario (e.g., greenhouse control or navigation). This review aims to fill this gap by providing a holistic and critical analysis of a wide spectrum of control algorithms, from PID (Proportional Integral Derivative) and fuzzy logic to ADRC (Active Disturbance Rejection control) and multi-objective optimization, in the context of crop production, pest management, machinery, resource optimization, and harvesting. Furthermore, we synthesize the overarching challenges hindering real-world deployment and identify emerging future directions, thereby offering a more structured and comparative perspective essential for guiding future research and large-scale adoption.

1.2. Reference Indexing Methods

The literature summarized in this review was collected by searching the Web of Science and Agricultural Science Literature Retrieval of Jiangsu University. We aimed to acquire the latest research outcomes in the field of control algorithms for intelligent agriculture over the last decade (2015–2025), encompassing reviews and research articles. The keywords were set in accordance with the content of each section. For Section 2, the topics set in the search engine were {“greenhouse climate control”}, {“precision irrigation”}, {“agricultural machinery navigation”}, {“identification of pests and diseases”}, {“integration of water and fertilizer”}, and so on. For Section 3, the following search topics were set: {“PID Control AND agriculture”}, {“fuzzy logic control AND greenhouse”}, {“Model Predictive Control (MPC) AND path tracking”}, {“adaptive control AND agricultural machinery”}, {“neural network AND image recognition”}, and so on. For Section 4 and Section 5, the entries set in the search engine were {“robustness of agricultural algorithms”}, {“edge computing in agricultural applications”}, {“digital twin agriculture”}, {“multisensor fusion”}, {“lightweight neural network”}, and so on. After organization, 87 articles were summarized in this review; among them, 84 papers were published from 2015 to 2025.

1.3. Manuscript Organization

The remainder of this manuscript is organized as follows: Section 1 elaborates on the research background, motivation, and literature indexing methods of intelligent agricultural control algorithms; Section 2 systematically classifies the application scenarios of agricultural control algorithms; Section 3 details the principles of traditional and advanced control algorithms and their specific applications in agriculture; Section 4 compares and analyzes the performance, advantages, and limitations of different algorithms, and summarizes the main challenges faced in practical applications; Section 5 looks forward to the future development directions of intelligent agricultural control algorithms in aspects such as algorithm fusion, technology integration, sustainability, and lightweight deployment; Section 6 summarizes the entire paper and emphasizes the importance of interdisciplinary cooperation in promoting agricultural intelligence and sustainable development.

2. Classification of Application Scenarios in Agriculture Control

As shown in Figure 1, the control issues in agriculture can be classified according to application scenarios into the following categories. Each category involves specific control requirements and technical solutions, covering a wide range of applications from traditional control algorithms to modern intelligent algorithms.

2.1. Crop Production

Greenhouse environment control, such as temperature and humidity regulation and CO₂ adjustment, is a core aspect of crop production. The combination of sensor technology and artificial intelligence is required to significantly enhance control accuracy [5], meeting the demand for data-driven decision-making in intelligent agriculture. Precise irrigation is crucial for resource optimization, relying on high-precision sensor data to support scheduling decisions [4]. Furthermore, the impact of climate change on crop production needs to be mitigated through optimized irrigation and greenhouse control [10], with practices such as no-till farming and cover crops improving water use efficiency in regions with unstable rainfall [11], aligning with the goal of reducing resource waste in sustainable agriculture.

2.2. Pest Management

Precision spraying relies on the accurate identification and dynamic regulation of pests and diseases, with image recognition technology being a core component. Effective systems must reduce pressure fluctuations and droplet size differences to improve spray coverage uniformity [2]. Distinguishing pest and disease targets in complex backgrounds presents a significant technical challenge [16], as does achieving real-time prediction of pest populations for timely intervention [17]. Accurate segmentation and counting of fruits under complex orchard conditions are also essential for tasks like sterile bud removal and harvest estimation [18].

2.3. Agricultural Machinery

Autonomous driving and path tracking are central to the intelligence of agricultural machinery. As illustrated in Figure 2, modern intelligent seeding machinery relies heavily on high-precision navigation systems to achieve uniform seed distribution, a fundamental requirement for crop production [4]. The implementation of advanced control systems is essential for automating operations such as sowing, harvesting, and spraying. These systems must compensate for unknown disturbances and maintain accuracy under varying terrain and operating conditions. Robotic harvesters further require robust locomotion and adaptive positioning for continuous autonomous operation in unstructured environments like orchards [19].

Figure 2. Schematic of the seeding machinery navigation [4].

Figure 2. Schematic of the seeding machinery navigation [4].

Liu et al. [4] used sliding mode control (SMC) to compensate for unknown disturbances, enabling the lateral tracking error of the tractor to be less than 0.2 m, demonstrating the advantages of sliding mode control in handling nonlinear problems. The MPC algorithm achieved centimeter-level accuracy in path tracking through dynamic models and rolling optimization [14], but its computational complexity is high and requires combined distributed design to improve real-time performance. Additionally, the harvesting robot achieved high-precision fruit positioning and grasping through YOLOv5 (You Only Look Once) and reinforcement learning [20], demonstrating the application of multi-algorithm integration in complex tasks.

2.4. Resource Optimization

Integrated water and fertilizer control is a key direction for resource optimization. Control systems must maintain precise concentration levels of fertilizer solutions while minimizing energy consumption [21,22]. The application of intelligent control strategies is crucial for improving energy utilization efficiency in low-carbon agriculture [23], meeting sustainable agriculture’s demand for optimized resource use.

2.5. Harvest and Sorting

Automated sorting relies on high-precision target detection and trajectory planning. Control systems for harvesting and sorting must achieve precise multi-parameter collaborative control (e.g., grain loss rate, impurity rate, breakage rate) [24] and accurate positioning for fruit picking through trajectory optimization [25], verifying the practicality of intelligent control in complex mechanical tasks.

3. Classification and Application of Control Algorithms

3.1. PID Control Algorithm

The PID (Proportional-Integral-Derivative) control algorithm, due to its simple structure, strong robustness, and ease of implementation, holds a significant position in the field of agricultural machinery automation control [4]. This algorithm achieves precise control of the system by the proportional section responding quickly to deviations, the integral section eliminating steady-state errors, and the derivative section predicting the trend of changes. In agricultural applications, this control method is particularly suitable for scenarios requiring high-precision positioning, such as the double-axis positioning tray conveying device developed by Yao et al. [26], which adopts a three-closed-loop PID control. After optimizing parameters through Simulink simulation, it achieved outstanding performance with the initial positioning deviation of the seedling trays being less than 1.34 mm in the X-axis and 0.99 mm in the Y-axis, significantly improving the operation accuracy of the automatic transplanting machine [26].

PID is particularly suitable for scenarios requiring high-precision positioning. For instance, in a double-axis positioning tray conveying device for automatic transplanting machines, a three-closed-loop PID control achieved initial positioning deviations of less than 1.34 mm (X-axis) and 0.99 mm (Y-axis) [26]. In tillage depth control, a system based on an STM32 microcontroller combining PID and an adaptive Kalman filter achieved stable control with a depth error of less than 1 cm, enhancing robustness to soil heterogeneity and vibrations [27]. In sowing operations, optimizing PID parameters with algorithms like PSO (particle swarm optimization) can significantly improve performance, as demonstrated by a fertilization stability coefficient of variation ≤0.91% [1].

However, traditional PID shows limitations with nonlinear and time-varying systems. For example, motor-driven sowing machines using PID experienced a significant increase in missed sowing rates at speeds exceeding 8 km/h due to response lag [28]. Similarly, in height adjustment for agricultural vehicles, traditional PID is prone to “overcharging” and continuous oscillation [6].

To address these limitations, hybrid strategies like fuzzy PID are widely used. A water and fertilizer ratio system using fuzzy PID reduced overshoot in EC and pH regulation by 70% and 42%, respectively, compared to traditional PID [29]. An irrigation scheduling algorithm combining sensor data and fuzzy logic effectively coped with soil heterogeneity [30]. More complex strategies, like feedforward PID (FPID) for spray boom attitude regulation, achieved a maximum inclination error of only 0.453° in complex terrains [31]. A B-PID adaptive controller for a tiger nut harvester based on the backstepping method showed maximum relative errors of only 3.8%, 3.67%, and 1.5% for speed, vibration frequency, and digging depth, respectively, significantly outperforming traditional methods [32].

Modern intelligent algorithms have also revitalized PID. Combining a GRU neural network’s prediction capability with fuzzy control’s real-time adjustment reduced energy waste by 90% in a pig house environment [8]. A speed feedforward PID algorithm brought faster response and higher accuracy to a spray bar active suspension system [7].

The application of PID control algorithm in intelligent agriculture demonstrates its high cost-effectiveness advantage. In standardized and highly linear operations (such as precise positioning and tillage depth control), it can provide excellent performance with extremely low computational overhead, making it highly suitable for deployment in resource-constrained embedded systems. However, this low cost and high real-time advantage comes at the expense of adaptability. When faced with the ubiquitous nonlinearity and time-varying characteristics in agricultural environments, its performance will significantly decline, leading to increased dropout rates or oscillations. Therefore, there is a clear trade-off in the application of PID algorithm: it is not suitable for complex dynamic scenarios, but its low cost and ease of use make it an ideal foundation for building high-performance, low-cost hybrid systems when integrated with intelligent algorithms (such as fuzzy logic, neural networks).

3.2. Fuzzy Logic Control

Fuzzy Logic Control (FLC), based on fuzzy set theory and reasoning, effectively manages nonlinear, uncertain, and multi-parameter coupled systems. Compared with traditional control methods, FLC simulates the human decision-making process by using fuzzy rules and membership functions to convert fuzzy inputs into precise outputs, and thus possesses stronger adaptability and robustness.

The core of fuzzy logic control consists of four steps: fuzzification, fuzzy rule base, fuzzy reasoning, and defuzzification. Common fuzzy reasoning models include the Mamdani type and the Sugeno type. The former is suitable for descriptive rules, while the latter has higher computational efficiency and is suitable for resource-constrained scenarios. For example, Fakhruroja et al. [33] used the Sugeno reasoning model and trapezoidal membership functions in an inorganic fertilizer cultivation system, requiring only 6 rules to dynamically adjust pH and humidity, achieving efficient growth of water spinach. Additionally, the fuzzy PID algorithm effectively addresses the stability issues of traditional PID in lagging systems. For instance, in the control of solid fertilizer dissolution, the steady-state error was reduced to 0.55 g/L [21].

In the field of agriculture, fuzzy logic control has been widely applied due to its ability to handle environmental uncertainties and achieve multi-objective optimization. In the intelligent control of combine harvesters, Chen et al. [34] proposed a multi-parameter fuzzy control method based on database knowledge discovery (KDD). By mining the association rules in sensor data (such as “IF the speed of the threshing drum is high THEN the loss rate is low”), they dynamically adjusted the weight factors of the cutting table screw, the conveying chute, and the threshing drum, ultimately increasing the feed rate by 9%, reducing the loss rate by 14%, and shortening the system adjustment time by 50%. In the aspect of precise irrigation, Bahat et al. [35] designed a fuzzy logic closed-loop controller that only required 44 rules to integrate multiple sources of input such as soil moisture, temperature, and wind speed, dynamically adjusting the valve opening, and controlling the soil moisture deviation within 3–4%, significantly reducing water resource waste.

Fuzzy logic control also demonstrates significant advantages in the environmental regulation of greenhouses and livestock industries. The fuzzy control system developed by Robles Algarin et al. [36], based on Arduino, achieves stable control of greenhouse environmental variables through multi-parameter collaborative regulation (with an error rate of less than 6%), and supports remote monitoring, verifying its applicability in resource-constrained scenarios. Mushtaq et al. [37] designed an environmental control system for livestock sheds, which dynamically regulates actuators through three input parameters (temperature, humidity, and air flow), with an error rate of less than 1.2%, demonstrating the robustness of fuzzy logic in multi-variable nonlinear systems. Additionally, as shown in Figure 3, Khudoyberdiev et al. [38] applied fuzzy logic to the energy and resource management of hydroponic systems, achieving a 18% reduction in energy consumption by dynamically adjusting the working level of actuators, highlighting its effectiveness in multi-objective optimization.

Figure 3. The general fuzzy logic control module for the proposed optimization based hydroponics model flowchart [38].

Figure 3. The general fuzzy logic control module for the proposed optimization based hydroponics model flowchart [38].

In the field of autonomous navigation for agricultural machinery, fuzzy logic control also performs exceptionally well. Simon [39] developed a self-balancing two-wheel drive unmanned system that combines the SLAM (Simultaneous Localization and Mapping) algorithm and fuzzy control. This system can achieve precise navigation and stable movement in complex greenhouse environments, effectively handling the uncertainty of sensor data and environmental dynamic changes, and enhancing the real-time performance and robustness of the system.

Fuzzy logic control performs exceptionally well in handling nonlinear systems and multi-parameter coupled problems. For instance, Yu et al. [40] designed a fuzzy controller in the torque distribution strategy of an electric tractor, dynamically adjusting the traction torque coefficient by inputting the opening degree of the accelerator pedal and its rate of change, achieving efficient power distribution for the tractor in different farming modes. Zhu et al. [41] designed a fuzzy PI controller in the energy management strategy of a hybrid tractor, significantly enhancing the robustness and fuel economy of the system by dynamically adjusting the ratio and integral coefficients of the oil-electric equivalent factor. This application validates the adaptability and robustness of fuzzy logic control in complex agricultural environments.

Although fuzzy logic control performs well in handling nonlinear, uncertain, and multi-variable coupled systems, its application still faces some fundamental challenges. The design of the fuzzy rule base heavily relies on expert experience and lacks a systematic construction method, which may lead to redundant or conflicting rules and affect the control performance. Additionally, the defuzzification step in the fuzzy reasoning process may introduce additional errors, especially in applications with high precision requirements (such as precise irrigation or environmental control). Although hybrid strategies like fuzzy PID have alleviated some of these issues to a certain extent, their overall performance is still limited by the heuristic nature of fuzzy logic and lacks a strict mathematical theoretical foundation. Especially in dynamic and highly variable agricultural environments, their adaptability and robustness still need to be further improved.

3.3. Neural Networks and Deep Learning

Neural networks and deep learning, as the core technologies of modern artificial intelligence, demonstrate extensive application potential in the agricultural field due to their powerful capabilities in feature extraction and pattern recognition. Neural networks, by mimicking the structure of human brain neuron connections, can learn nonlinear relationships from complex data. Meanwhile, deep learning, through multi-level network architectures (such as convolutional neural networks, CNN), further enhances the performance in processing high-dimensional data (such as images, time series data).

In agricultural image recognition, CNNs perform exceptionally well. For instance, an improved Faster R-CNN model (Figure 4) with ResNet-50 and Soft-NMS significantly enhanced the detection accuracy (mAP 90.7%) of key tomato organs, reducing memory usage and detection time [16]. Agricultural-specific pre-trained models (e.g., AgFT, Xception, Inception-ResNet) improved the F1 score by 0.47% in weed recognition and reduced the training period by 13.67% compared to general models, highlighting the importance of domain adaptation [42]. Weed-crop recognition systems on UAVs (Unmanned Aerial Vehicle) and robots have achieved over 90% accuracy [43].

Figure 4. Improved framework for the Faster R-CNN model [16].

Figure 4. Improved framework for the Faster R-CNN model [16].

In pest detection, innovative network structures solve small target detection problems. An anchor-free region CNN (AF-RCNN) achieved 56.4% mAP and 85.1% recall on a 24-pest species dataset, improving by 7.5% and 15.3% over Faster R-CNN, with a detection time of 0.07 s per image [44]. Combining Bayesian methods with CNNs enhanced uncertainty handling, achieving accuracy rates of 92.1% and 92.4% in beetle and locust classification tasks, enabling lightweight probabilistic model deployment on edge devices [45].

In plant disease identification, deep learning overcomes challenges of small samples and resource constraints through data augmentation and lightweight design. A four-layer CNN combined with a GAN for data augmentation achieved over 99% accuracy for tomato leaf disease detection with a model size of only 36 MB, suitable for real-time field detection [46]. HRNet performed semantic segmentation on drone-captured field images, classifying soil, healthy vegetation, and diseased vegetation with 93–94% accuracy, though issues like light interference and early disease misclassification remain [47]. Fine-tuned CNN and Transformer models achieved up to 100% classification accuracy on grape leaf disease datasets [48].

In agricultural environment prediction and regulation, neural networks combined with optimization algorithms demonstrate strong adaptability. A GA-optimized BP neural network (GA-BP) predicted crop water requirement based on real-time greenhouse data (temperature, humidity, light intensity) with significantly lower error (MAE = 0.0273, RMSE = 0.0396) than traditional methods [49]. Optimized CNNs on Raspberry Pi 3B achieved a strawberry detection system with 1.63 FPS and 84.2% average accuracy, proving the practicality of lightweight models in resource-constrained scenarios [50].

Deep learning algorithms have demonstrated unparalleled accuracy advantages in tasks such as image recognition and prediction, which is beyond the reach of traditional algorithms. However, this outstanding performance comes at a high cost of data, computation, and economic resources. Model training requires a large amount of high-quality labeled data, which is expensive and time-consuming to collect and label; deployment requires higher computing resources, thereby increasing hardware costs. This makes deep learning more suitable for solutions in high-value economic crops or large agricultural enterprises at the current stage. The application trade-off lies in: in scenarios that pursue ultimate accuracy and have sufficient budgets, it is the preferred solution; but in large-scale farms with limited data or those that are cost-sensitive, the feasibility and cost-effectiveness of its deployment still need to be improved.

3.4. Model Predictive Control

Model Predictive Control (MPC) is an advanced control algorithm based on dynamic models, rolling optimization, and feedback correction. With its ability to explicitly handle multiple constraints and nonlinear systems, it demonstrates significant advantages in fields such as autonomous navigation, path tracking, and environmental regulation of agricultural machinery. MPC effectively addresses uncertainties and complex dynamic characteristics in agricultural scenarios by predicting the future behavior of the system and optimizing control inputs online. Depending on system requirements, MPC can be classified into linear MPC (LMPC), nonlinear MPC (NMPC), and distributed MPC (DMPC), providing flexible solutions for various agricultural applications [13].

In the field of agricultural machinery path tracking, MPC demonstrates outstanding performance through high-precision dynamic modeling and real-time optimization. For instance, He et al. [14] designed linear error objectives and constraint functions based on the machine tool attitude correction model, achieving high-precision control for a paddy field transplanting machine, and verifying the adaptability of MPC in dynamic paddy fields. For complex terrains, Liu et al. [51] proposed an adaptive variable-parameter MPC algorithm (AMPC), which combines the recursive least squares method to real-time correct tire parameters and dynamically adjusts weight coefficients, reducing the peak lateral error of unmanned agricultural machinery in mountainous environments by 67%, significantly improving trajectory tracking accuracy and lateral stability. Similarly, Zhou et al. [52] used a genetic algorithm to dynamically optimize the MPC time-domain parameters, reducing the maximum lateral deviation of articulated steering tractors in U-shaped and complex curve paths by 59.0% and 13.2%, respectively, highlighting the superiority of adaptive MPC in dynamic environments.

In the autonomous navigation of agricultural machinery, the multi-objective optimization capability of MPC has been further verified. Wang et al. [53] proposed an adaptive MPC method combining fuzzy control and particle swarm optimization (PSO) for rear-wheel steering agricultural machinery. As shown in Figure 5, by dynamically adjusting the prediction time domain and control parameters, they achieved high-precision tracking with a lateral error mean of ≤0.18 cm under complex paths, providing a solution for the lightweight deployment of edge devices [53]. Lin et al. [54] designed an MPC controller based on the lateral dynamics model and adopted the cross-line steering method in the narrow space of orchards. They controlled the average tracking errors of straight and curved paths within 1.8 cm and 5.44 cm, significantly outperforming the traditional U-shaped steering strategy, and also pointed out the practicality of the simplified model for real-time control. Additionally, Manikandan et al. [55] proposed the Curve Perception MPC (C-MPC) which dynamically adjusted the speed of AGV (Automated Guided Vehicle) to reduce the RMSE (Root Mean Square Error) of lateral and longitudinal tracking errors to 0.85 m and 0.62 m, respectively, providing an efficient method for multi-scenario agricultural machinery navigation.

Figure 5. PSO-MPC control flow [53].

Figure 5. PSO-MPC control flow [53].

MPC also demonstrates potential in sowing and resource optimization management. The field experiment conducted by Liu et al. [4] showed that MPC, by iteratively optimizing future trajectories, reduced the deviation of sowing row spacing by 15%. Zhang et al. [56] pointed out that by combining the characteristic of biochar to improve soil water retention, the water use efficiency of the irrigation system can be further enhanced through the collaborative optimization of MPC and fuzzy control.

The core advantage of MPC lies in its ability of feedforward optimization and explicit handling of constraints, enabling it to achieve centimeter-level accuracy in dynamic optimization problems such as path tracking and resource management. However, this predictive capability comes at the cost of high computational complexity. The real-time performance of MPC heavily depends on the accuracy of the model and the scale of the optimization problem, and usually requires higher processor performance, which limits its application on low-cost hardware. Therefore, there is a typical trade-off between performance and computing resources in the application of MPC. It is highly suitable for systems with extremely high control accuracy requirements and the ability to provide sufficient computing power platforms (such as high-end unmanned tractors, greenhouse central control systems), but for small, low-cost agricultural equipment, its lightweight version (such as linear MPC) or combination with other low-cost algorithms is a more realistic choice.

3.5. Self-Adaptive Control

Adaptive control algorithms are a class of advanced control methods that can automatically adjust control parameters according to the dynamic changes in the system. They demonstrate unique advantages in complex nonlinear agricultural systems. These algorithms effectively address the uncertainties and time-varying characteristics in the agricultural environment through online parameter identification and real-time control strategy adjustments. They mainly include types such as model reference adaptive control (MRAC), self-tuning control, and adaptive control based on neural networks.

In the field of agricultural machinery control, adaptive algorithms have significantly enhanced the system’s robustness in complex terrains. Lu et al. [57] designed an adaptive backstepping controller for tracked robots. By combining the online adjustment of parameters using a BP neural network and compensating for slip parameters with a soft-switching sliding mode observer, they reduced the steering trajectory error to a level where the simulation error was less than 1%, providing an effective solution for high-precision trajectory tracking in agricultural environments. Wang et al. [58] proposed a Pure Pursuit algorithm based on dynamic forward distance, which adjusted the forward distance (1.5–4.5 m) in real time, enabling the tracked sprayer in hilly and mountainous areas to achieve an average tracking error of 2.15 cm at an operating speed of 0.7 m/s, verifying the control capability of adaptive algorithms in complex terrains.

In the field of precision agriculture operations, adaptive control demonstrates excellent dynamic adjustment performance. Gao et al. [59] designed a fertilization axis rotational speed self-calibration system based on segmented linear interpolation, which reduced the steady-state error of fertilization control to 0.13 r/min and the overshoot to 1.54%, significantly improving the fertilization accuracy. Fernandez et al. [60] proposed a self-regulating controller combined with the recursive least squares parameter identification method, which can adapt to the changes in tractor speed and traction force fluctuations, and still maintains a stable closed-loop response in variable soil conditions.

In the fields of agricultural environment control and biological treatment, adaptive control also performs exceptionally well. Luo et al. [61] developed an adaptive control strategy based on FCASM (Fully coupled activated sludge model), which improved the total nitrogen removal rate by 25.11% by real-time optimization of parameters such as dissolved oxygen and sludge age, and maintained the total phosphorus removal rate at over 98%, significantly outperforming traditional fixed-parameter strategies. In dynamic operations such as fruit harvesting, Mehta and Burks [62] proposed an adaptive control strategy based on Lyapunov stability theory, which increased the harvesting success rate by 20% through online learning of unknown motion parameters.

Furthermore, the integration of intelligent algorithms and adaptive control has further expanded the application potential. Wang and Noguchi [63] proposed a dynamic circular turning algorithm based on reinforcement learning, which reduced the turning time and trajectory length by 17% and 21%, respectively, by real-time estimation of the turning radius (average error ≤ 3.9 cm). Liu et al. [51] developed an adaptive variable-parameter MPC algorithm, which significantly improved the trajectory tracking accuracy of unmanned agricultural machinery in mountainous environments by real-time correction of tire parameters and dynamic adjustment of weight coefficients.

Adaptive control significantly enhances the adaptability to the dynamic changes in the agricultural environment through online parameter adjustment and system identification. However, it still has obvious limitations in practical applications. The complexity and computational burden of the parameter identification algorithm are high, and it is difficult to achieve real-time deployment on resource-constrained edge devices. Moreover, the stability analysis of the adaptive control system is relatively difficult, especially in the presence of unmodeled dynamics or external strong disturbances, which may lead to system instability or performance degradation. Although combining neural networks and optimization algorithms can improve these issues to some extent, the overall reliability and practicality still need to be verified over a long period in more real agricultural scenarios.

3.6. Active Disturbance Rejection Control

Active Disturbance Rejection Control (ADRC) is a novel control strategy based on disturbance estimation and compensation. Its core idea is to uniformly estimate the internal and external disturbances of the system as “collective disturbances” through an Extended State Observer (ESO), and to compensate them in real time, thereby significantly enhancing the robustness of the system in complex environments. ADRC mainly consists of a Tracking Differentiator (TD), an Extended State Observer, and a Nonlinear Feedback (NF) component. It has significant advantages such as low dependence on mathematical models and strong anti-interference ability [64]. In the agricultural field, ADRC and its linearized form (LADRC) have been widely applied in scenarios such as precise pesticide application, agricultural machinery navigation, and water and fertilizer regulation, providing effective solutions to the time-varying and strong disturbances in agricultural environments.

In the field of agricultural machinery control, ADRC demonstrates outstanding anti-interference performance. Ji et al. [65] designed an accurate pesticide application system using the LADRC method. Through the linear extended state observer (LESO), it can real-time estimate the disturbances caused by vehicle speed changes and pipeline pressure fluctuations. Compared with traditional PID, the response speed is increased by 3–5 s, and the steady-state error is reduced by 2–9%. Lin et al. [66] proposed a combined control strategy of ADRC-PSO, which estimates unmodeled dynamics and external disturbances through ESO and optimizes parameters using the particle swarm algorithm. This enables agricultural vehicles to reduce the steady-state error by 60% when the mass changes by 50% and significantly improves the response speed. Wang et al. [67] developed a composite anti-interference algorithm (ACDRC) that combines ESO and the inverse model disturbance observer, which enhances the anti-interference performance of agricultural drones under wind disturbances by 82.5%, providing a reliable guarantee for ultra-low-altitude operations.

In precision agriculture operations, ADRC achieves highly accurate resource regulation. Tang et al. [68] proposed the IPSO-LADRC strategy, which improves the particle swarm algorithm to dynamically adjust the controller parameters, reducing the speed adjustment time of the electric reel sprinkler irrigation machine to 0.064 s and controlling the fluctuation range within 0.3%, significantly enhancing the control accuracy under variable speed conditions. Peng et al. [69] addressed the multi-channel coupling problem of the water and fertilizer integrated machine, using ADRC for flow proportion control, effectively suppressing the interference between pipelines and achieving higher precision regulation than traditional PID.

In terms of the integration of intelligent optimization algorithms, ADRC demonstrates strong adaptability. The research by Tu et al. [64] shows that ADRC can achieve a 2 s steering adjustment and an accuracy of 1° in orchard spraying robots, significantly outperforming traditional PID. As shown in Figure 6, Ji et al. [65] further improved the performance of the spraying system in dynamic disturbance scenarios by optimizing the parameters of LADRC through the particle swarm algorithm. These studies have verified the potential of integrating intelligent optimization with ADRC.

Figure 6. The structural block diagram of LADRC [65].

Figure 6. The structural block diagram of LADRC [65].

ADRC uniformly estimates and compensates both internal and external disturbances through the expansion state observer (ESO), demonstrating strong anti-interference capabilities. However, its parameter tuning process is complex and heavily relies on optimization algorithms (such as PSO), making it difficult to automatically adapt to different working conditions in practical applications. Moreover, ESO is sensitive to high-frequency noise, which may cause system oscillations, especially when sensor accuracy is insufficient or communication delay is large. Although LADRC (linear active disturbance rejection control) simplifies parameter design, its performance still depends to a certain extent on the accuracy of the system model. In extremely nonlinear or time-varying systems, its disturbance estimation accuracy and control effect may be limited.

3.7. Sliding Mode Control

Sliding Mode Control (SMC) is a highly robust nonlinear control method. Its core concept is to design a sliding surface to make the system state converge within a finite time and maintain stable motion on the sliding surface. Sliding mode control is inherently insensitive to parameter variations and external disturbances, making it particularly suitable for the complex and variable environments in agricultural applications. Depending on the design method, sliding mode control can be classified into traditional sliding mode control, terminal sliding mode control, and high-order sliding mode control, etc. It has demonstrated significant advantages in fields such as agricultural machinery control, path tracking, and environmental regulation.

In the field of agricultural machinery control, sliding mode control has overcome the operational challenges in complex terrains through its strong robustness. Wang et al. [70] developed a sliding mode synchronous control method based on a disturbance observer for crawler-operated machinery in hilly and mountainous areas. By using a hydraulic interconnection framework structure and nonlinear disturbance compensation, they achieved a synchronous error of ±6 × 10⁻⁴ m, reducing the leveling time by 35.5% compared to traditional PID, significantly enhancing the safety and efficiency of steep slope operations. Matveev et al. [71] proposed a nonlinear sliding mode controller that effectively reduced the jitter phenomenon of traditional sliding mode control by combining equivalent control with smooth nonlinear laws, ensuring the global convergence and stability of autonomous tractors in complex terrains.

In the field of path tracking, sliding mode control has achieved high-precision control through improved algorithm design. Sun et al. [72] proposed the fixed-time non-singular terminal sliding mode control (ADO-FTTSM) method based on the adaptive disturbance observer (ADO), which combines the fixed-time convergence theory, enabling the steady-state error of unmanned agricultural tractors to be ≤0.2 m. At the same time, it avoids the jitter problem of traditional sliding mode control. Xue et al. [73] designed a sliding mode controller for differential-drive agricultural robots, achieving a maximum tracking error of 0.21 m through the switching function and exponential convergence law, verifying its robustness and real-time performance in complex farmland environments. Ji et al. [74] developed a composite adaptive terminal sliding mode controller (FDOB-CATSMC) that estimates disturbances through a finite-time observer, reducing the lateral tracking error convergence time to within 1 s and making the steady-state error close to zero.

In terms of the integration of intelligent algorithms, sliding mode control demonstrates greater adaptability. Shen et al. [75] proposed sliding mode control based on fuzzy extended state observer (FESO-SMC), which, by dynamically adjusting the parameters of the ESO, reduced the attitude control error of the agricultural unmanned helicopter to one fifth of that of the traditional sliding mode control, and increased the position tracking accuracy by more than twice. Yin et al. [76] designed an adaptive sliding mode control method, which dynamically adjusted the approaching rate of the sliding mode surface through adjustable parameters, effectively suppressing the jitter phenomenon while maintaining high-precision path tracking performance. Ge et al. [15] proposed adaptive sliding mode control (ASMC), which significantly improved the tracking accuracy of unmanned agricultural vehicles in variable road conditions by real-time estimation of the tire lateral stiffness boundary.

Sliding mode control demonstrates excellent performance in agricultural machinery control due to its strong robustness and insensitivity to parameter variations and external disturbances. However, traditional sliding mode control suffers from significant “jerk” problems, which can be alleviated through higher-order sliding mode or by combining with observers, but still cannot be completely eliminated, affecting the lifespan of actuators and the smoothness of control. Moreover, the design of the sliding surface depends on the system dynamics model, and in cases of significant model uncertainty, the control performance may decline. Although the introduction of intelligent algorithms (such as fuzzy logic, neural networks) enhances its adaptive ability, the overall design complexity and dependence on model information remain obstacles to widespread application.

3.8. Multi-Objective Optimization

Multi-objective optimization algorithms are key technologies for solving complex decision-making problems in agriculture. They can simultaneously optimize multiple conflicting objective functions and provide a decision-making space through the Pareto optimal solution set. These algorithms mainly include methods based on evolutionary computation and swarm intelligence algorithms (such as PSO, gray wolf algorithm), and have demonstrated strong optimization capabilities in fields such as agricultural machinery design, resource allocation, and environmental regulation.

In the design of agricultural machinery and trajectory planning, multi-objective optimization algorithms have achieved a balance between performance and efficiency. Li et al. [25] proposed an improved trajectory planning method based on the NSGA-III (Non-dominated Sorting Genetic Algorithm) algorithm, which, by integrating cubic splines and quintic polynomial interpolation, achieved Pareto-optimal solutions in three objectives: the time, energy consumption, and jerk of the kiwifruit harvesting robotic arm, with a trajectory success rate exceeding 93%. Qu et al. [77] combined the NSGA-III algorithm and the TOPSIS method to optimize the dual-arm suspension system of agricultural robots, achieving multi-objective collaborative optimization of lightweighting (total mass 1.39 kg), stability (center of gravity height 0.338 m), and shock absorption performance. Xia et al. [78] used the NSGA-II algorithm to optimize the continuously variable transmission of agricultural tractors, increasing the zero-speed torque ratio by 70.22% while only reducing the equivalent efficiency by 2.87%.

In the field of optimizing precision agriculture operations, multi-objective algorithms demonstrate significant decision-making advantages. Fathollahi-Fard et al. [79] proposed the NSGEA algorithm based on genetic engineering improvement, which processed uncertainties through fuzzy logic and optimized the economic benefits, waste, and carbon emissions targets in blueberry harvesting. Huang et al. [80] combined the fuzzy analytic hierarchy process with the improved wolf algorithm and achieved coordinated optimization of soil fragmentation rate, tillage depth stability, and energy consumption in the optimization of rotary tillage parameters for electric horticultural tractors, reducing the number of iterations by 5 times and increasing the accuracy by 20.33%. Wang et al. [81] optimized the structural parameters of the wheat seed distributor using the PSO algorithm, reducing the seed damage rate by 15.9% and improving the uniformity of seed distribution by 41.5%.

In the field of intelligent regulation of agricultural environments, multi-objective optimization offers innovative solutions. Liu et al. [82] proposed a method based on Gaussian mixture models and an improved NSGA-II algorithm, which achieved precise regulation of greenhouse light environment by maximizing crop photosynthetic rate and minimizing supplementary light energy consumption (prediction error RMSE = 0.7641). Dong et al. [83] developed the AMOEA (advanced multi-objective evolutionary algorithm) algorithm, which provided an efficient solution for task allocation of agricultural robots through heuristic initialization and local search strategies.

Multi-objective optimization algorithms provide effective tools for solving complex decision-making problems in agriculture, especially in resource allocation, mechanical design, and environmental regulation. However, their computational cost is high, especially in high-dimensional problems. The running time of algorithms such as NSGA-III significantly increases, making it difficult to meet real-time control requirements. Moreover, the selection of Pareto solution sets often relies on the subjective preferences of decision-makers, lacking objective and unified evaluation criteria, which may affect the practicality and acceptability of the final solution. Although the application of heuristic algorithms and surrogate models has improved efficiency to some extent, their real-time performance and adaptability in dynamic environments remain key challenges for future research.

4. Comparative Analysis and Challenges

4.1. Comparative Analysis

The selection of an optimal control algorithm for an intelligent agricultural system involves critical trade-offs between performance, cost, and practicality. To elucidate these compromises, Table 1 synthesizes and contrasts the fundamental characteristics of prevalent algorithms. It highlights their respective strengths and, crucially, their limitations—such as sensitivity to environmental dynamics, computational demands, and implementation challenges—providing a foundational guide for informed decision-making in both research and deployment.

Table 1. Comparison of Different Algorithms.

Algorithm	Main Feature	Application Scenarios	Advantage	Limitation	Refs.
PID Control	Simple, robust, three-loop feedback control.	Depth control, speed adjustment, environmental regulation.	Fast response, easy to implement and tune.	Poor performance with nonlinear, time-varying systems.	[4,6,26]
Fuzzy Logic	Rule-based, handles imprecise information and uncertainty.	Irrigation, multi-parameter optimization, climate control.	No precise model needed; handles multi-variable coupling.	Rule design relies on expertise; defuzzification can cause errors.	[21,34,35]
Neural Networks	Powerful feature extraction and pattern recognition from data.	Pest/disease ID, fruit sorting, yield prediction.	High accuracy in complex tasks (e.g., >90% mAP).	Requires large, labeled datasets; high computational cost.	[16,42,44]
Model Predictive Control (MPC)	Uses a dynamic model for predictive, constrained optimization.	Path tracking, navigation, resource optimization.	Handles constraints explicitly; high precision control.	Computationally intensive; performance depends on model accuracy.	[13,14,53]
Adaptive Control	Adjusts parameters online to cope with system dynamics.	Machinery control in varying conditions, precise operations.	High robustness to changing environments.	Complex stability analysis; tuning can be difficult.	[57,59,60]
Active Disturbance Rejection (ADRC)	Estimates and compensates for total disturbances via an ESO.	Spraying, drone control, pressure/flow regulation.	Strong anti-interference; low dependency on precise model.	Parameter tuning is complex; sensitive to high-frequency noise.	[65,66,68]
Sliding Mode Control (SMC)	Forces system state to slide on a predefined surface.	Synchronization, path tracking, attitude control.	Very robust to disturbances and parameter variations.	inherent “chattering” problem; requires system model.	[15,70,72]
Multi-Objective Optimization	Optimizes multiple conflicting objectives simultaneously.	Machinery design, resource allocation, path planning.	Provides Pareto-optimal solutions for complex trade-offs.	Computationally expensive; solution selection can be subjective.	[25,77,82]

Table 1. Comparison of Different Algorithms.

Algorithm	Main Feature	Application Scenarios	Advantage	Limitation	Refs.
PID Control	Simple, robust, three-loop feedback control.	Depth control, speed adjustment, environmental regulation.	Fast response, easy to implement and tune.	Poor performance with nonlinear, time-varying systems.	[4,6,26]
Fuzzy Logic	Rule-based, handles imprecise information and uncertainty.	Irrigation, multi-parameter optimization, climate control.	No precise model needed; handles multi-variable coupling.	Rule design relies on expertise; defuzzification can cause errors.	[21,34,35]
Neural Networks	Powerful feature extraction and pattern recognition from data.	Pest/disease ID, fruit sorting, yield prediction.	High accuracy in complex tasks (e.g., >90% mAP).	Requires large, labeled datasets; high computational cost.	[16,42,44]
Model Predictive Control (MPC)	Uses a dynamic model for predictive, constrained optimization.	Path tracking, navigation, resource optimization.	Handles constraints explicitly; high precision control.	Computationally intensive; performance depends on model accuracy.	[13,14,53]
Adaptive Control	Adjusts parameters online to cope with system dynamics.	Machinery control in varying conditions, precise operations.	High robustness to changing environments.	Complex stability analysis; tuning can be difficult.	[57,59,60]
Active Disturbance Rejection (ADRC)	Estimates and compensates for total disturbances via an ESO.	Spraying, drone control, pressure/flow regulation.	Strong anti-interference; low dependency on precise model.	Parameter tuning is complex; sensitive to high-frequency noise.	[65,66,68]
Sliding Mode Control (SMC)	Forces system state to slide on a predefined surface.	Synchronization, path tracking, attitude control.	Very robust to disturbances and parameter variations.	inherent “chattering” problem; requires system model.	[15,70,72]
Multi-Objective Optimization	Optimizes multiple conflicting objectives simultaneously.	Machinery design, resource allocation, path planning.	Provides Pareto-optimal solutions for complex trade-offs.	Computationally expensive; solution selection can be subjective.	[25,77,82]

To quantitatively evaluate the core performance of different control algorithms in intelligent agriculture applications, Table 2, based on the experimental data reported in the main text, systematically summarizes and compares the key indicators of eight mainstream algorithms. The quantitative results presented in this table (such as Accuracy, Error reduction and Robustness) are all derived from specific case studies in various literature (see the Refs. column). These data provide objective and direct decision-making basis for researchers and practitioners to weigh algorithm performance against implementation costs according to specific application scenarios (such as high-precision positioning, resistance to interference in complex environments, multi-objective resource optimization, etc.).

Table 2. Quantitative parameter comparison among different algorithms.

Algorithm	Accuracy	Error Reduction	Robustness	Refs.
PID Control	≤1.34 mm (positioning)	—	Moderate	[4,26,27]
Fuzzy Logic	Soil moisture deviation ≤ 3–4%	Overshoot reduced by 70% (EC), 42% (pH)	High (multi-variable coupling)	[21,34,35]
Neural Networks	mAP ≥ 90.7% (tomato detection)	Recall improved by 15.3%	High (complex backgrounds)	[16,42,44]
Model Predictive Control (MPC)	RMSE 0.043 m (path tracking)	Lateral error reduced by 67%	High (explicit constraint handling)	[13,14,53]
Adaptive Control	Steering error ≤ 3.9 cm	Trajectory error < 1% (simulation)	Very high (dynamic environments)	[57,59,60]
Active Disturbance Rejection (ADRC)	Speed fluctuation ≤ 0.3%	Steady-state error reduced by 2–9%	Very high (anti-interference)	[65,66,68]
Sliding Mode Control (SMC)	Synchronization error ± 6 × 10−4 m	Leveling time reduced by 35.5%	Extremely high (insensitive to disturbances)	[15,70,72]
Multi-Objective Optimization	Trajectory success rate > 93%	Seed damage reduced by 15.9%	Provides Pareto-optimal solutions	[25,77,82]

Notes: EC: Electrical Conductivity is often used to measure the ion concentration in water and fertilizer solutions. pH: Potential of Hydrogen (Acidity/Alkalinity), an indicator measuring the acidity or alkalinity of a solution. mAP: Mean Average Precision (MAAP) is a commonly used metric for evaluating the accuracy of object detection models. The higher the value, the better the model performs. Recall: (Recall rate) refers to the proportion of positive samples that the model correctly identifies out of all the actual positive samples. RMSE: Root Mean Square Error, which is used to measure the deviation between the predicted values and the actual values, the lower the value, the better.

Table 2. Quantitative parameter comparison among different algorithms.

Algorithm	Accuracy	Error Reduction	Robustness	Refs.
PID Control	≤1.34 mm (positioning)	—	Moderate	[4,26,27]
Fuzzy Logic	Soil moisture deviation ≤ 3–4%	Overshoot reduced by 70% (EC), 42% (pH)	High (multi-variable coupling)	[21,34,35]
Neural Networks	mAP ≥ 90.7% (tomato detection)	Recall improved by 15.3%	High (complex backgrounds)	[16,42,44]
Model Predictive Control (MPC)	RMSE 0.043 m (path tracking)	Lateral error reduced by 67%	High (explicit constraint handling)	[13,14,53]
Adaptive Control	Steering error ≤ 3.9 cm	Trajectory error < 1% (simulation)	Very high (dynamic environments)	[57,59,60]
Active Disturbance Rejection (ADRC)	Speed fluctuation ≤ 0.3%	Steady-state error reduced by 2–9%	Very high (anti-interference)	[65,66,68]
Sliding Mode Control (SMC)	Synchronization error ± 6 × 10−4 m	Leveling time reduced by 35.5%	Extremely high (insensitive to disturbances)	[15,70,72]
Multi-Objective Optimization	Trajectory success rate > 93%	Seed damage reduced by 15.9%	Provides Pareto-optimal solutions	[25,77,82]

Notes: EC: Electrical Conductivity is often used to measure the ion concentration in water and fertilizer solutions. pH: Potential of Hydrogen (Acidity/Alkalinity), an indicator measuring the acidity or alkalinity of a solution. mAP: Mean Average Precision (MAAP) is a commonly used metric for evaluating the accuracy of object detection models. The higher the value, the better the model performs. Recall: (Recall rate) refers to the proportion of positive samples that the model correctly identifies out of all the actual positive samples. RMSE: Root Mean Square Error, which is used to measure the deviation between the predicted values and the actual values, the lower the value, the better.

Overall, the selection of algorithms should be balanced according to the priority of the specific application scenarios: if high accuracy is required and sufficient computing resources are available, MPC or deep learning can be chosen; if strong robustness and anti-interference ability are needed, ADRC or SMC are more suitable; in scenarios with cost sensitivity and high real-time requirements, PID or a combined strategy of PID and intelligent algorithms is more practical.

4.2. Challenges

The control algorithms in intelligent agriculture, although demonstrating significant advantages, still encounter multiple challenges in practical deployment, mainly manifested in the following aspects:

4.2.1. The Complexity and Uncertainty of the Agricultural Environment

The dynamic changes in the farmland environment (such as soil heterogeneity, weather fluctuations, and mechanical vibrations) impose strict requirements on the robustness and adaptability of the algorithms [6]. This variability means that an algorithm performing well in one condition may suffer from performance degradation, such as overshooting, oscillations, or model mismatch, in another [84]. Compensating for these effects, for instance by reducing trajectory errors under slip conditions, often necessitates high-precision sensors and complex techniques, thereby increasing the system’s overall cost and complexity [57].

4.2.2. Hardware Costs and Computing Power Limitations

The performance of intelligent algorithms often relies on the high-precision sensors and computing power of edge devices. However, the high hardware costs limit their application in small-scale farms [66]. Furthermore, there is a fundamental trade-off between the computational complexity of advanced algorithms (e.g., the optimization required for ADRC tuning or the long calculation times of NSGA-III [77]) and the need for real-time, low-cost operation on resource-constrained platforms. This often forces a compromise between algorithmic sophistication and practical feasibility.

4.2.3. Data Scarcity and Annotation Costs

Deep learning algorithms perform exceptionally well in tasks like pest and disease identification, but they require a large amount of high-quality labeled data for training [16]. The acquisition and annotation of such data in agricultural scenarios are costly and time-consuming. Moreover, models often face a “reality gap” or lack generalization ability, where performance trained in one environment (e.g., on internet data) declines when deployed in actual field conditions, highlighting the critical need for diverse and representative datasets [85].

4.2.4. Real-Time Performance and Dynamic Response Requirements

The real-time requirements of agricultural operations (such as a 10% decrease in classification accuracy when image blurring occurs when the pesticide application machine’s traveling speed exceeds 3 km/h) impose strict limitations on algorithm deployment [86]. Although MPC performs well in path tracking, its computational load is high (such as the prediction time domain for 15 steps), and it needs to be reduced by lightweight design (such as TinyMPC) to lower the computing power requirements [14]. This once again demonstrates the direct trade-off between the length of the prediction time domain (directly related to control performance) and the computational burden. In scenarios with extremely high real-time requirements, it may be necessary to sacrifice some prediction accuracy (shortening the prediction time domain) in order to achieve a faster response speed.

4.2.5. Multi-Objective Collaboration and Algorithm Integration

Agricultural systems involve multi-variable coupling, requiring the balancing of conflicting objectives (e.g., coordination of subsystems in a combine harvester [81], or optimizing for both productivity and sustainability). While multi-objective optimization algorithms can provide a set of solutions, the final decision-making can be subjective. Furthermore, integrating different intelligent algorithms with traditional control (e.g., fuzzy PID, neural network optimization MPC) to achieve a balance between robustness, real-time performance, and economy remains a complex systems engineering challenge [29].

In conclusion, overcoming these challenges requires a holistic approach that moves beyond the optimization of individual algorithms. Future efforts must focus on the co-design of hardware and software, the development of more efficient and generalizable models, and stronger interdisciplinary collaboration between agricultural science, control engineering, and computer science to enable scalable and sustainable solutions [12,30].

4.2.6. Human Factors and User Acceptance

The successful deployment of intelligent agricultural systems is not solely a technical challenge but also a human-centric one. The ultimate end-users—farmers and agricultural workers—play a critical role in the adoption and effective utilization of these advanced technologies [87,88]. Key human factor-related challenges include:

• Usability and Human–Machine Interaction (HMI): Complex control interfaces and data visualization dashboards can be intimidating for users without a technical background. Poorly designed HMIs can lead to operational errors, reduced efficiency, and ultimately, rejection of the technology [89]. Ensuring intuitive interaction and providing clear, actionable insights rather than raw data are crucial for user acceptance.
• Trust and Transparency: The “black-box” nature of some advanced algorithms (e.g., deep learning) can erode user trust. Farmers are often reluctant to rely on systems whose decision-making logic they cannot understand [90]. Developing explainable AI (XAI) techniques and providing transparent operational logic are essential for building confidence in automated recommendations and actions.
• Skills Gap and Training Needs: The operation, maintenance, and troubleshooting of intelligent agricultural machinery require a new set of digital skills. The existing agricultural workforce may lack these skills, creating a significant barrier to adoption [87]. Comprehensive training programs and ongoing technical support are necessary to bridge this gap and ensure the sustainable use of advanced systems.
• Socio-Economic Impact and Behavioral Change: The transition to Agriculture 4.0 may alter traditional farming practices and workflows. Concerns about job displacement, economic viability for smallholder farms, and the willingness to change established practices are critical socio-economic factors that can hinder widespread adoption [88]. Addressing these concerns requires not only technological solutions but also policy support and demonstrations of clear economic benefits.

Overcoming these human-factor challenges necessitates a user-centered design approach from the outset, where farmers are involved in the development process. Interdisciplinary collaboration must extend beyond engineering and computer science to include social scientists, economists, and human-factor experts to ensure that the solutions are not only technologically sophisticated but also practical, acceptable, and beneficial for the end-users.

5. Future Direction

Future control algorithms must overcome the deployment barriers discussed in Section 4.2, including environmental complexity, hardware limitations, data scarcity, real-time demands, and multi-objective coordination. The following directions represent the most promising pathways to bridge the gap between algorithmic potential and widespread practical adoption.

5.1. Algorithm Innovation and Integration

Future algorithm development will focus on creating solutions that are inherently more adaptable and efficient, directly addressing the challenges of environmental uncertainty and hardware constraints. In terms of traditional algorithm optimization, Chen et al. [6] proposed the adaptive PSD algorithm with a single neuron PID controller, which provided a new paradigm for intelligent control of agricultural machinery. Zhu et al. [27] also confirmed that the PID control combined with IMU (Inertial Measurement Unit) sensors and adaptive Kalman filter can still achieve centimeter-level plowing depth accuracy on edge devices, demonstrating a path to high performance without excessive computational cost.

In terms of the integration of intelligent algorithms, hybrid strategies are key to enhancing robustness without sacrificing real-time performance. Zheng et al. [91] proposed a mixed strategy of trapezoidal speed control and Kalman filter tracking, which laid the foundation for multi-agent collaborative control. Xue et al. [73] pointed out that the combination of sliding mode control and fuzzy logic can effectively reduce tracking errors. Li et al. [92] combined RBF neural networks with sliding mode control, achieving high-precision path tracking of rice transplanters in muddy environments through online parameter self-tuning and disturbance estimation, thus tackling the challenges of nonlinearity and terrain-induced disturbances. To overcome the critical barrier of data scarcity, transfer learning will become a key technology to solve the problem of algorithm generalization, and cross-crop model verification shows that the VAF (variance-accounted-for) difference can be controlled within 9.1% [24].

The standardization construction of agricultural-specific pre-trained models is also a direct response to the high cost of data annotation and model training. Espejo-Garcia et al. [42] suggested establishing an agricultural model repository similar to TensorFlow Hub to accelerate AI application development, which would significantly lower the entry barrier for small-scale farms.

5.2. Technological Integration

Technology integration is essential for creating robust systems that can perceive complex environments and make coordinated decisions, thereby mitigating the risks of operational failure. Digital twin technology, for example, provides a virtual testing ground to overcome the high cost and risk of field testing for complex algorithms. Wang et al. [70] verified its value in the hydraulic leveling system through a joint simulation using AMESim (2021.1)-MATLAB/Simulink (2021b), and the combination of C-MPC algorithm and digital twin can significantly reduce the cost of field tests [28]. Wang et al. [93] achieved closed-loop control by building a digital twin model of the broad bean harvesting system and combining CPN (colored Petri net), realizing virtual simulation and real-time optimization, providing new ideas for dynamic decision-making in complex agricultural scenarios.

The breakthrough in multi-sensor fusion technology is a direct solution to enhance perception reliability in heterogeneous and uncertain environments. Cui et al. [7] developed a method for integrating ultrasonic and IMU data to enhance environmental perception. Liu et al. [4] pointed out that high-spectrum and near-infrared sensors may become new breakthrough points. Furthermore, to meet the real-time requirements of distributed systems, 5G/6G networks will drive the development of remote real-time control, as Zhu et al. [94] proposed an enhanced Bluetooth protocol optimization scheme that provides a reference for network topology design. Multi-agent collaborative control (e.g., Gao et al.‘s [59] case of unmanned tractors and variable fertilizer application machines) provides a architectural solution to the challenge of scaling up automation while managing multi-objective conflicts across different operational units.

5.3. Sustainability

The integration of control algorithms with sustainable practices addresses the multi-objective challenge of optimizing for both productivity and environmental impact. Low-carbon agricultural technologies exhibit a trend of integrating algorithms with ecology. Zhang et al. [56] confirmed that the combination of biochar and water-saving irrigation controlled by intelligent algorithms could increase the total soluble solids (TSS) of tomatoes by 42.33%. Tang et al.’s [68] LADRC algorithm controlled the speed fluctuation within 0.3%, significantly reducing water resource waste. Energy consumption optimization is a critical objective that algorithms can directly achieve. Adaptive control achieved a balance between nitrogen removal efficiency and energy consumption by dynamically adjusting the aeration volume in wastewater treatment [61]. To enhance climate resilience, He et al. [10] suggested combining digital twin simulation with climate adaptation strategies to cope with the uncertainty of extreme weather. Raheem et al. [11] emphasized the importance of integrating traditional agricultural wisdom with modern technology for sustainable development.

5.4. Lightweighting and Edge Computing

This direction is perhaps the most direct response to the barriers of hardware costs, computing power limitations, and real-time performance. Lightweight model development has achieved a series of breakthroughs aimed at deploying advanced algorithms on affordable hardware: for instance, Xiao et al. [95] proposed a lightweight detection method for blueberry ripeness based on the improved YOLOv5. This method maintains high accuracy while significantly reducing the model size and computational cost, providing a feasible solution for real-time recognition of mobile devices in orchards. Guerrero-Ibanez et al. [46] compressed the model to 36 MB by optimizing the network structure, and Li et al. [85] used partial convolution strategies in APNet to significantly reduce computational complexity.

Edge deployment solutions have been continuously innovated to enable real-time control on low-power devices. Zhu et al. [1] based on the STM32F4 PID system met the NY/T 1003-2006 standard [96], Sun et al. [72] implemented an adaptive controller on the STM32 MCU with a response speed of 0.006 s. These advances are crucial for making intelligent agriculture feasible for small-scale farms.

The lightweight implementation of multi-agent collaboration has become possible. Wang et al. [53] used PSO to optimize MPC parameters to provide a solution for heterogeneous agricultural machinery collaboration, effectively distributing computational load. Liu et al. [86] proved that data augmentation can effectively improve the generalization ability of the model, mitigating data scarcity issues. Omaye et al. [97] pointed out that the unmanned aerial vehicle-agricultural machinery collaboration system will promote the large-scale application of real-time disease monitoring, and Jiao et al.‘s [44] anchor-free detection design opened up a new path for mobile device deployment.

6. Conclusions

Control algorithms are pivotal in advancing intelligent agriculture, demonstrating significant efficacy across diverse domains such as crop production, pest management, autonomous machinery, resource optimization, and harvesting. While traditional PID control remains valuable for its simplicity and rapid response in structured environments, its limitations in handling nonlinear and time-varying agricultural systems necessitate integration with intelligent algorithms like fuzzy logic and neural networks. Advanced methods such as MPC excel in precision tasks like path tracking and greenhouse control, whereas adaptive control, ADRC, and sliding mode control enhance robustness in dynamic and uncertain environments.

Key insights from this review include:

• Hybrid Algorithm Integration is Essential: No single algorithm universally addresses the complexity of agricultural systems. Future efforts should prioritize hybrid strategies (e.g., fuzzy-PID, neural network-enhanced MPC) that combine the robustness of traditional methods with the adaptability of AI-driven approaches, thereby balancing performance, cost, and real-time requirements.
• Data Efficiency and Lightweight AI are Critical Barriers: The high cost of data annotation and computational demands of deep learning models limit their scalability. Research should focus on developing lightweight neural networks, transfer learning frameworks, and agricultural-specific pre-trained models to enable deployment on resource-constrained edge devices and small-scale farms.
• Hardware-Software Co-Design is Needed for Real-World Deployment: Overcoming challenges related to environmental variability, real-time processing, and multi-sensor integration requires closer collaboration between control engineers and hardware developers. Innovations in low-cost sensors, edge computing platforms, and digital twin simulations will be essential to validate and deploy algorithms under realistic conditions.
• Sustainability Must Be Embedded in Algorithm Design: Future control systems should explicitly optimize for energy efficiency, water conservation, and carbon reduction. Multi-objective optimization algorithms should be leveraged to balance agricultural productivity with environmental impacts, supporting the transition to climate-resilient and sustainable farming practices.

Priorities for future research include:

• Developing low-cost, high-accuracy sensors for real-time environmental and crop monitoring.
• Designing lightweight and transferable AI models that require minimal labeled data.
• Advancing multi-agent and distributed control systems for coordinated field operations.
• Integrating digital twins for virtual testing and optimization of control strategies before real-world deployment.

Through interdisciplinary collaboration and a focus on these strategic priorities, control algorithms will play a transformative role in achieving precise, efficient, and sustainable agricultural systems.