Overview of Agricultural Machinery Automation Technology for Sustainable Agriculture

Abstract

Automation in agricultural machinery, underpinned by the integration of advanced technologies, is revolutionizing sustainable farming practices. Key enabling technologies include multi-source positioning fusion (e.g., RTK-GNSS/LiDAR), intelligent perception systems utilizing multispectral imaging and deep learning algorithms, adaptive control through modular robotic systems and bio-inspired algorithms, and AI-driven data analytics for resource optimization. These technological advancements manifest in significant applications: autonomous field machinery achieving lateral navigation errors below 6 cm, UAVs enabling targeted agrochemical application, reducing pesticide usage by 40%, and smart greenhouses regulating microclimates with ±0.1 °C precision. Collectively, these innovations enhance productivity, optimize resource utilization (water, fertilizers, energy), and mitigate critical labor shortages. However, persistent challenges include technological heterogeneity across diverse agricultural environments, high implementation costs, limitations in adaptability to dynamic field conditions, and adoption barriers, particularly in developing regions. Future progress necessitates prioritizing the development of lightweight edge computing solutions, multi-energy complementary systems (integrating solar, wind, hydropower), distributed collaborative control frameworks, and AI-optimized swarm operations. To democratize these technologies globally, this review synthesizes the evolution of technology and interdisciplinary synergies, concluding with prioritized strategies for advancing agricultural intelligence to align with the Sustainable Development Goals (SDGs) for zero hunger and responsible production.

1. Introduction

Agriculture, as the cornerstone of human survival and development, faces multifaceted challenges in the 21st century. The Food and Agriculture Organization (FAO) projects a 60% increase in global food demand by 2050, while finite arable land, water scarcity, and frequent extreme climate events persistently constrain agricultural productivity [1]. Concurrently, the aging and migration of rural labor forces have intensified globally, with 68% of cultivated land in developing countries still reliant on traditional manual operations. Within this context, agricultural machinery automation [2,3] emerges as a pivotal solution for sustainable development, offering core advantages in cost-effectiveness, precision management, and environmental adaptability [4,5].

Recent advancements in automation technologies, centered on intelligent perception–decision–execution closed-loop systems [6], have fundamentally transformed agricultural production through interdisciplinary integration and scenario-specific innovations. At the perception level, multispectral imaging, LiDAR, and high-precision sensor networks establish multidimensional data acquisition matrices [7]. For instance, 3D LiDAR-equipped rice–wheat-harvesting robots achieve 98.7% obstacle recognition accuracy and complete path replanning within 300 ms, significantly enhancing operational safety in complex field environments [8]. Decision-layer innovations employ deep learning and reinforcement learning algorithms to decipher dynamic correlations between soil nutrients, crop physiology, and environmental parameters. Notably, LSTM network-driven irrigation systems demonstrate 27% improvement in water use efficiency and 15% reduction in nitrogen application for maize cultivation, marking a paradigm shift from experience-based to data-driven agriculture [9]. The execution layer witnesses collaborative operations among modular robots, UAV swarms, and autonomous agricultural machinery, overcoming the spatiotemporal limitations of conventional equipment [9]. Blockchain-optimized scheduling systems reduce response time to one-third of traditional approaches.

Despite these technological breakthroughs, the large-scale implementation of agricultural automation confronts critical challenges, including technical heterogeneity, cost barriers, and regional adaptability. While RTK-GPS achieves centimeter-level positioning accuracy in open fields, its effectiveness diminishes in greenhouses or under dense canopies [10]. Visual navigation systems, though superior in orchard environments, face limitations in algorithmic real-time performance and illumination sensitivity [11]. Moreover, high retrofit costs for smallholder equipment and low technology adoption rates in developing countries underscore the conflict between technical accessibility and economic viability [12]. However, existing reviews fail to holistically address three key gaps: (i) interdisciplinary synergies between agronomy and engineering, (ii) scalable solutions for heterogeneous farm sizes, and (iii) the integration of green energy systems with automation.

This review systematically examines the technological evolution, application scenarios, and interdisciplinary synergies in agricultural machinery automation. By synthesizing fragmented research achievements, it constructs a comprehensive framework bridging theoretical innovation and practical implementation. The analysis not only delineates research frontiers in intelligent perception, autonomous navigation, and energy optimization, but also provides industrial references for equipment development, system integration, and business model innovation. Critically, it offers three unique contributions: (i) a unified taxonomy of automation technologies across diverse farm scales, (ii) actionable pathways for SDG-aligned sustainable farming, and (iii) evidence-based policy recommendations to democratize global adoption—addressing the gaps left by prior works. Ultimately, this work aims to contribute to global food security and the realization of the Sustainable Development Goals (SDGs), particularly zero hunger and responsible production.

2. Key Technologies for Agricultural Machinery Automation

2.1. Positioning Technology

The rapid development of agricultural automation technology has provided crucial support for addressing global agricultural labor shortages, enhancing production efficiency, and improving resource utilization [13,14]. As a core component, positioning and navigation technologies demonstrate diversified application patterns and technical challenges across different agricultural scenarios. According to the review by Xie et al. [15]. (as shown in Figure 1), current agricultural positioning technologies are primarily classified into three categories: global navigation, local environmental perception navigation, and hybrid navigation, each forming differentiated solutions tailored to specific scenario requirements. The global navigation mode relies on satellite positioning systems (e.g., GPS, BDS, GLONASS), demonstrating exceptional performance in structured environments like open farmlands [16]. Through Real-Time Kinematic (RTK) technology, centimeter-level accuracy can be achieved [17]. For instance, Lipiński et al. [18] developed an autonomous tillage machine employing single-mode satellite positioning with navigation errors below 6 cm. Zhang et al. [19] integrated RTK-BDS with an Inertial Navigation System (INS), enhancing positioning accuracy to 3 cm in complex terrains. However, satellite signals are susceptible to obstruction in environments such as greenhouses, fruit tree canopies, or tunnels, leading to positioning failures. To address the cross-regional operation requirements of decentralized smallholder plots, Xiao et al. [20] designed a low-cost navigation system based on Ultra-Wideband (UWB) base station technology. Through dual-base station deployment, they achieved seamless positioning with 5 cm accuracy, providing a feasible pathway for small-scale agricultural mechanization [21,22].

Local environmental perception navigation plays a dominant role in complex scenarios with limited satellite signals, such as orchards and protected agriculture [23]. Visual navigation achieves dynamic path planning by capturing crop row or facility structural features through cameras. For example, Zhang et al. [24] utilized CCD cameras to detect rice seedling row paths, while Guan et al. [25] integrated binocular cameras to realize adaptive navigation for wheat combine harvesters in low-contrast scenarios. However, the reliability of visual systems is constrained by illumination variations and the real-time processing limitations of algorithms [26]. Light Detection and Ranging (LiDAR), with its high resolution and anti-interference capabilities, has become the preferred solution for orchard navigation [27]. Jones et al. [28] achieved inter-row path planning by fitting LiDAR point clouds in kiwifruit orchards, with an average lateral deviation of less than 2 cm. Liu et al. [29] further employed 3D LiDAR to construct environmental maps and integrated a Region of Interest (ROI) algorithm to address the spatial information loss inherent in traditional 2D LiDAR, significantly improving navigation stability in densely planted jujube orchards. Notably, innovative applications of Ultra-Wideband (UWB) technology in protected agriculture offer novel approaches for indoor global positioning. Lin et al. [30] established a local coordinate system by deploying multiple reference nodes, achieving a positioning accuracy of ±10 cm in greenhouse environments. Meanwhile, Ju et al. [31] developed a photoelectric array lateral navigation system, which realized high-precision obstacle avoidance with 2 cm accuracy in corridor settings through an arc-shaped sensor configuration.

A hybrid navigation mode, which integrates multi-sensor fusion to leverage both global and local advantages, has emerged as a research hotspot in complex agricultural scenarios [32]. Bakker et al. [33] addressed the global coverage challenges of local navigation by fusing satellite navigation with visual perception. Meanwhile, Wei et al. [34] integrated RTK-GPS and depth cameras in combine harvesters, achieving dynamic path optimization through the multi-feature recognition of crop height and lodging status. As an auxiliary technology, inertial navigation (IMU) compensates for cumulative errors in satellite or radar systems via short-term high-precision attitude measurements. For instance, Zhang et al. employed a loosely coupled integration of GNSS and IMU data, utilizing Kalman filtering to correct heading angle deviations, thereby improving the positioning stability of orchard spray robots by 40%. In livestock farming scenarios, the disinfection robot designed by Champaikon et al. [35] combined RFID tags with magnetic navigation technology to achieve autonomous obstacle avoidance in complex barn corridors. In the aquaculture field, Shen et al. developed an unmanned patrol vessel that employs sonar and vision fusion technology to perform dual tasks of water quality monitoring and cage inspection in pond environments, demonstrating breakthroughs in cross-medium navigation.

Future agricultural positioning technologies are expected to exhibit three major trends: First, artificial intelligence-driven multi-modal perception will advance environmental adaptability [36]. For example, Ji et al. [37] achieved the semantic segmentation of farmland obstacles using 3D LiDAR and convolutional neural networks (CNNs), while Xie et al. [38] proposed an environment-adaptive algorithm based on attention mechanisms to dynamically adjust sensor weights in response to abrupt illumination changes. Second, the miniaturization and integration of low-cost, high-precision sensors will broaden accessibility. Radiocaj et al. [39] evaluated the performance of low-cost GNSS modules in hilly terrains, offering economical solutions for smallholder farmers. Third, intelligent multi-machine collaboration and self-repair systems will enhance operational resilience. Li et al. [40] explored task-priority-based path allocation algorithms for multiple agricultural machines. IoT-supported remote diagnostic systems enable fault prediction through multi-parameter fusion (e.g., vibration, temperature), ensuring uninterrupted 24 h autonomous operations. Additionally, the deep integration of agronomy and machinery will be pivotal for practical implementation. For instance, Mao et al. [41] developed an orchard transport robot that combines human posture recognition with inter-row navigation, enabling a flexible “stop-and-go” operational mode, highlighting the potential of context-adaptive technologies. In summary, agricultural automation positioning technologies are evolving from single-function systems toward intelligent and networked architectures (Figure 2) [15]. Their advancement relies not only on hardware innovation, but also on interdisciplinary collaboration, which aligns with agricultural scenarios and production demands, ultimately driving the comprehensive realization of precision agriculture [42,43].

2.2. Perceptive Technology

The perception technology framework in agricultural automation comprises three core components: image acquisition, processing, and classification. Its technological advancements and representative applications profoundly influence the intelligentization of modern agriculture [44,45]. In the image acquisition stage, diversified sensor technologies provide precise data support for diverse scenarios. Monocular vision systems are widely adopted for 2D detection due to their cost-effectiveness [46,47]. For instance, Roy et al. [48] implemented apple recognition through color and texture features. However, its lack of depth information has driven the development of binocular vision systems. Scholars such as Naser et al. [49] utilized binocular cameras to construct 3D environmental maps, significantly enhancing the autonomous navigation capabilities of agricultural machinery. Multispectral and hyperspectral imaging technologies enable refined crop monitoring by capturing physiological characteristics. Examples include Honrado’s NDVI-based farmland monitoring [50] and Kumar’s pest and disease identification [51], as shown in Figure 3. Additionally, thermal imaging assesses water stress through leaf temperature differentials [52], while X-ray technology enables the internal structure analysis of grains via penetration detection [53]. Together, these technologies establish a multidimensional agricultural perception network [54].

Image processing technologies transform raw data into actionable information through a three-tiered processing workflow, as illustrated in Figure 4. Low-level processing focuses on data optimization. For example, Senni et al. [55] applied Wiener filtering for denoising, while Mustafa et al. [56] demonstrated the importance of preprocessing through banana ripeness feature detection. Mid-level processing extracts target features via segmentation algorithms. Li et al. [57] proposed a blueberry cluster segmentation method, and Meng et al. [58] addressed overlapping apple detection using a watershed algorithm. High-level processing relies on machine learning for intelligent decision-making. Pan et al. [59] achieved 97% accuracy in identifying mechanical damage in refrigerated peaches using an MLPANN (Multilayer Perceptron Artificial Neural Network) model, highlighting the algorithm’s effectiveness in complex scenarios. Classification algorithms, as the core of system intelligence, exhibit complementary strengths between traditional machine learning and deep learning. Support vector machines (SVMs) and fuzzy logic remain practical for crop classification, while deep networks like CNNs excel in complex object recognition. For instance, William et al. [60] developed a kiwifruit detection system based on CNNs, and Jiao et al. [61] enhanced Mask R-CNN to achieve 97.31% segmentation accuracy for apples, demonstrating the superiority of deep learning in precision agriculture.

Typical applications exemplify the practical value of perception technologies. In weed control, Chang et al. [63] developed a YOLOv3-based weed recognition robot achieving 90% accuracy, while Wu et al. [64] optimized operational paths using a multi-camera tracking system enhanced by an Extended Kalman Filter (EKF) algorithm. For harvesting tasks, Blok et al. [65] implemented the GrabCut algorithm for selective broccoli harvesting, and Zhang et al. [66] achieved 96% accuracy in tomato maturity grading. In soil analysis, Rahimi-Ajdadi et al. [67] demonstrated the innovative potential of combining RGB images with Adaptive Neuro-Fuzzy Inference Systems (ANFISs) for non-contact soil moisture prediction. Current technologies still face challenges such as complex lighting adaptability and real-time processing efficiency. Future trends will focus on lightweight model deployment (e.g., MobileNet), multi-modal sensor fusion [68], and blockchain–IoT integrated autonomous decision systems [69]. These advancements will drive agricultural automation toward more intelligent and reliable paradigms [70,71].

2.3. Control and Execution Technology

The core of agricultural automation technology lies in efficient control strategies [72,73] and precise execution technologies [74,75]. With the rapid development of modular robots, multi-robot systems, and bio-inspired algorithms, agricultural production is progressively advancing toward intelligence and sustainability [76,77]. The control architecture of agricultural robots directly impacts their task execution efficiency and environmental adaptability. According to the classification by Kvalsund et al. [78], control strategies can be divided into the following three categories. First, centralized control systems operate under the principle of a single central controller coordinating all modular actions, optimizing decisions through global information. Their strength lies in high-precision task synchronization, making them suitable for structured environments like greenhouses. For example, the Agrobot SW6010 employs centralized control to achieve precise strawberry harvesting. Its vision system identifies ripe fruits using RGB-D cameras, while the central controller coordinates robotic arm trajectories, limiting positioning errors to within ±2 mm and damage rates below 3% [79]. However, a critical limitation is its reliance on the stability of the central node—communication delays or failures may lead to system breakdowns. A case study involving autonomous tractors demonstrated that centralized control-induced communication interruptions caused seeding path deviations, underscoring the need for redundant designs to enhance fault tolerance.

The second category is decentralized control [80,81], where individual modules make autonomous decisions based on local sensor data and neighborhood communication to achieve self-organization. Its primary advantage lies in its enhanced adaptability to dynamic environments. For instance, the Thorvald II robot employs a decentralized control architecture, where each module uses IMU and LiDAR to perceive terrain in real time, autonomously adjusting speed and paths in muddy farmlands. This approach improved task completion rates by 22% [82]. Furthermore, decentralized control systems are supported by advanced algorithms such as artificial potential field-based path planning [83] and reinforcement learning [84], which have been widely adopted to optimize decentralized decision-making in agricultural robotics.

The third category is hybrid control, which operates on the principle of a central system planning global tasks while submodules autonomously optimize local behaviors [85,86]. For instance, the SwarmBot robot integrates centralized task allocation with a distributed Ant Colony Algorithm (ACA) to achieve multi-robot collaborative spraying in cornfields. The central system delineates spray zones, while sub-robots dynamically adjust their paths through pheromone simulation, reducing total travel distance by 28% and energy consumption by 15% [87], as shown in Figure 5.

Execution technologies are realized through modular robots and bio-inspired algorithms. Modular robots adapt to diverse tasks through dynamic reconfiguration [88]. For example, the ElectroVoxel module employs electromagnets to reconfigure itself within seconds, assembling into chain structures for seeding or lattice structures for heavy-load transportation [89]. The Module-W robot utilizes shape memory alloys (SMAs) for actuation, enabling adaptation to irregular terrain within crop rows [90]. The Agrobot SW6010 is equipped with interchangeable end-effectors: a strawberry-picking module combines vacuum suction and flexible grippers, while a disease detection module integrates a hyperspectral camera, with module switching time under 5 min [91]. However, modular robots face commercialization challenges. Peck et al. [92] highlight that their costs are 3–5 times higher than traditional equipment, coupled with insufficient energy efficiency (average operational endurance of only 4 h). Addressing these issues requires optimizing lightweight designs and energy management algorithms [93].

Bio-inspired algorithms have demonstrated significant technological advantages in the field of agricultural intelligence, effectively addressing collaborative control and dynamic optimization challenges in complex scenarios through the simulation of natural biological behavioral mechanisms [94]. Taking the Ant Colony Optimization (ACO) algorithm as an example, this approach achieves groundbreaking advancements in path planning for autonomous spraying robot systems in California vineyards by simulating the swarm intelligence characteristics of pheromone deposition and volatilization observed in ant colonies. Experimental data reveal that the ACO algorithm dynamically optimizes the total operational path length of robots from 1.2 km to 0.89 km, exhibiting a 40% improvement in iterative convergence speed compared with traditional genetic algorithms [87].

In collaborative operation scenarios, the Harvest CROO robotic cluster, leveraging the Ant Colony Optimization (ACO)-based task priority allocation mechanism, achieves the dynamic scheduling of multi-robot parallel harvesting by constructing a distributed decision-making network in strawberry fields, demonstrating a sixfold improvement in operational efficiency compared to manual harvesting. Concurrently, the Particle Swarm Optimization (PSO) algorithm exhibits unique value in UAV swarm coordination. Rossides et al. [95] developed a UAV swarm system that employs an adaptive velocity vector adjustment mechanism, accomplishing 95% area coverage in a 100 × 100 m cotton field monitoring mission while reducing task duration by 35% compared to random search strategies. Regarding environmental adaptability, Parada et al. [96] innovatively implemented a Modular Neural Network (MNN) architecture in the ATRON modular robot. This system dynamically adjusts greenhouse irrigation strategies through online learning mechanisms that perceive real-time physiological state changes in tomato plants, successfully enhancing water resource utilization efficiency by 18%. These studies collectively demonstrate that bio-inspired algorithms, through swarm intelligence, self-organizing learning, and dynamic optimization capabilities, are accelerating the evolution of agricultural production toward precision and autonomy [97,98].

2.4. Artificial Intelligence and Data Analysis Technology

As a cornerstone of agricultural automation, artificial intelligence (AI) and data analytics technologies address the critical limitations of traditional methods by enabling real-time decision-making, predictive modeling, and adaptive control. Their integration resolves challenges such as dynamic environmental variability, resource inefficiency, and labor-intensive data processing. For instance, machine learning (ML) optimizes full-lifecycle crop management through supervised and unsupervised algorithms, while deep learning (DL) revolutionizes complex tasks like disease detection and yield forecasting. These capabilities position AI as a pivotal enabler of precision agriculture, transforming raw data into actionable insights for sustainable farming [99,100]. As one of the core tools, machine learning (ML) optimizes full-lifecycle crop management through supervised and unsupervised learning algorithms [101]. In crop classification and recommendation domains, support vector machines (SVMs) and random forests demonstrate notable advantages: Zheng et al. [102] leveraged Landsat NDVI time-series data to achieve over 86% classification accuracy for nine crop types in complex cropping systems using an SVM, while random forests, with their feature selection capabilities, were enhanced by Geng et al. [103] for food safety risk prediction. By employing virtual sample augmentation to enhance data diversity, their approach significantly improved model robustness, as illustrated in Figure 6. Soil fertility and water resource management rely on decision trees and their variants. For instance, Leroux et al. [104] proposed the Minimal Universal Scalable Tree (MUST), which utilizes linear discriminant analysis to generate oblique splitting hyperplanes, reducing decision tree complexity while increasing classification accuracy by 15%. Additionally, ensemble learning techniques integrate multi-algorithm strengths through model stacking (Stacking) [105]. Cao et al. [106] combined SVM with artificial neural networks (ANNs) in a crop recommendation system, achieving 98% accuracy while mitigating the overfitting issues inherent in single models. Nevertheless, data quality limitations (e.g., feature redundancy and annotation deficiencies) remain primary challenges. Huang et al. [107] addressed this by developing a Relief-F feature-weighted SVM to eliminate weakly correlated features, reducing the number of support vectors by 30% while maintaining classification performance [108].

Deep learning (DL) demonstrates revolutionary potential in complex agricultural scenarios due to its multi-level feature extraction capabilities [109,110]. As an efficient single-hidden-layer feedforward network, the extreme learning machine (ELM) significantly accelerates training speeds by leveraging the random initialization of hidden layer weights and the analytical computation of output weights, achieving a fivefold improvement over traditional backpropagation networks. Suruliandi et al. [111] successfully applied an ELM to crop classification based on soil parameters (nitrogen, phosphorus, potassium, etc.). Radial Basis Function Networks (RBFNs), known for rapid convergence, excel in soil fertility prediction. Amirian et al. [112] optimized RBFN performance by employing K-means clustering to determine hidden layer centers, enabling 90% coverage efficiency in low-dimensional datasets.

In image analysis, convolutional neural networks (CNN) exhibit exceptional crop disease recognition capabilities. Pallathadka et al. [113] compared SVM, Naïve Bayes, and CNN for rice disease detection, revealing that CNN’s local receptive field architecture enhances complex texture recognition, achieving 92% accuracy. For time-series forecasting, recurrent neural networks (RNN) and their Long Short-Term Memory (LSTM) variants model long-term dependencies through memory units. Ouafiq et al. [114] demonstrated an 18% reduction in prediction error compared to ARIMA models in yield forecasting, particularly excelling under climate variability scenarios. Apolo et al. [115] trained a Faster R-CNN deep learning model to detect, count, and predict citrus fruit sizes accurately, while also utilizing LSTM-based detection to estimate fruit counts per tree, as illustrated in Figure 7. However, DL’s performance heavily relies on high-quality annotated data. Wang et al. [116] addressed this limitation by proposing a dimensionality reduction method combining Principal Component Analysis (PCA) with ELM, reducing data redundancy by 40%. Concurrently, Internet of Things (IoT) sensor networks enhance model inputs by providing real-time dynamic parameters (e.g., temperature, humidity, soil pH), effectively compensating for the limitations of traditional static datasets [117,118].

Time-series analysis provides scientific foundations for precision agriculture decision-making by uncovering trends and periodicity in historical data. Traditional ARIMA models demonstrate robust performance in linear forecasting. Padhan et al. [119] developed an ARIMA model based on Indian crop yield data (1950–2010), successfully predicting tea and cardamom production with mean absolute percentage errors (MAPE) as low as 4.2% and 5.8%, respectively. To overcome linear limitations, Xu et al. [120] utilized a deep learning-based stacked auto-encoders (SAE) algorithm to extract spectral features from hyperspectral imaging (HSI) data of grapes, combined with size compensation factors, and then employed PLS and LSSVM models to predict TSS and TA, achieving high accuracy with the SAE-LSSVM model. Hewamalage et al. [121] systematically compared these approaches, showing that RNN reduces short-term yield prediction errors by 12% compared to conventional methods, while LSTM outperforms in long-term climate impact forecasting through its gating mechanisms that mitigate gradient vanishing issues. Nevertheless, data gaps and overfitting remain critical challenges. Badmus et al. [122] addressed missing values in Nigerian maize yield data using cubic spline interpolation, narrowing ARIMA prediction confidence intervals by 15%. For RNNs, dropout regularization reduced overfitting risks by 30%, while the deployment of edge computing devices enabled real-time field data processing, minimizing cloud transmission delays [123].

2.5. Green Energy Technology

The demand for energy and power technologies in agricultural automation is growing increasingly urgent, particularly against the backdrop of reducing fossil fuel dependence, lowering carbon emissions, and enhancing production efficiency [124]. The application of renewable energy technologies has become pivotal in this context. Solar, wind, and hydropower, as primary technological pathways, demonstrate diverse application scenarios in agricultural automation. However, they face challenges such as technical adaptability, economic feasibility, and regional disparities. This article systematically elucidates their technological characteristics and practical progress through literature-based case studies [125].

2.5.1. Solar Technology

Solar energy, owing to its widespread availability and technological maturity, has emerged as one of the most promising renewable energy sources for agricultural automation [126]. In irrigation, solar irrigation pumps (SIPs) directly drive water pumps via photovoltaic (PV) panels, significantly reducing operational costs and carbon emissions. For instance, Bangladesh promoted solar irrigation projects through government subsidies, installing 1186 SIPs by 2018 to cover major agricultural regions. Each pump reduced CO₂ emissions by approximately 22 kg annually while lowering irrigation costs by 20–30% compared to traditional diesel pumps. India scaled up adoption under its “National Solar Mission,” achieving 181,000 SIP installations by 2019, particularly in arid regions with groundwater depths up to 75 m. However, SIPs face challenges such as unstable power supply during rainy seasons and limited suitability for low-lift, small-scale irrigation. For land-use intensification, agrivoltaic (APV) systems integrate PV panels above farmland to enable dual-purpose “energy generation + crop production,” as shown in Figure 8 [127]. German studies indicate that APV systems can increase land-use efficiency by 35–73%, while reducing soil evaporation and enhancing soil moisture by 328% due to shading effects. In China, policies like value-added tax incentives have spurred PV greenhouse construction, though high initial investment costs and yield reductions in shade-sensitive crops like corn remain barriers. Solar drying technologies have also advanced. Indirect solar dryers improve efficiency by 40%, while semi-transparent PV greenhouses in Italy and Australia demonstrate payback periods shortened to 9 years. However, addressing the performance degradation of PV components under high-temperature conditions is critical for long-term viability [128].

2.5.2. Wind Energy Technology

Wind energy has been applied in agricultural automation primarily for irrigation, mechanical drive, and distributed power supply, as illustrated in Figure 9. In irrigation, Turkey’s National Energy Efficiency Action Plan facilitated the adoption of wind-powered irrigation, with 3500 wind turbines installed by 2019, achieving a total capacity of 7600 MW. However, wind variability necessitates complementary energy storage systems, and turbine noise poses significant disturbances to wildlife. Traditional windmills in the Netherlands remain operational for grain milling, though their efficiency is below 30%. Japan has experimented with integrating micro-wind turbines into farmlands, but further policy efforts are needed to reduce renewable energy prices for broader market penetration. For distributed power supply, Australian dairy farms reported 15% energy cost savings using 30 kW wind systems, while Germany’s Renewable Energy Act (EEG) incentivizes farmers to sell wind-generated electricity, advancing rural energy self-sufficiency. Nevertheless, wind energy’s geographic dependency limits its universal applicability, as a minimum wind speed of 7–9 m/s is required for scalable deployment, a condition unmet in many regions [129,130].

2.5.3. Hydroelectric Technology

Hydropower, as the most traditional renewable energy source, is predominantly utilized in agriculture for irrigation and mechanical drive [127,131]. China promoted small hydropower development under its 13th Five-Year Plan, achieving a hydropower installed capacity of 300 GW by 2020. However, climate change has reduced river runoff by 1.7–2%, intensifying conflicts between irrigation and power generation over water resources. In Japan’s Tochigi Prefecture, artificial irrigation systems integrated with micro-hydropower turbines have enhanced water-use efficiency for rice production. Meanwhile, Nepal and Bhutan employ micro-hydropower turbines to drive milling machinery, reducing operational costs by 60% compared to diesel-powered equipment. Challenges persist, particularly in developing countries, where the lack of standardized equipment hampers technology dissemination. Additionally, hydropower plants may trigger ecological issues, such as the disruption of fish migration and watershed ecological imbalance. Turkey, leveraging its 433 GWh hydropower potential, has advanced agricultural electrification, but seasonal runoff fluctuations remain a critical technical bottleneck [132]. The foundational technologies described above collectively enable the application scenarios discussed in the following section, where we map innovations from Section 2.1 to Section 2.5 to specific agricultural use cases.

Green energy technologies are grouped as a unified key technology due to their shared objective: decarbonizing agricultural automation through renewable power sources. They mitigate fossil fuel dependence, reduce operational costs, and align with sustainable farming goals. Solar, wind, and hydropower represent complementary solutions tailored to regional resource availability, forming an integrated approach to energy resilience in agriculture.

3. Application Scenarios and Cases

With the rapid penetration of agricultural automation key technologies throughout the entire industry chain, their application scenarios have evolved beyond single-link optimization to establish intelligent solutions covering the entire “cultivation–management–harvest” cycle. Supported by three core technological frameworks—intelligent perception, autonomous decision-making, and collaborative control—autonomous agricultural machinery is redefining field operation patterns. Precision farming systems are revolutionizing resource allocation logic, while intelligent equipment clusters are transcending the spatial–temporal boundaries of agricultural production [133]. This section systematically deconstructs five pivotal application scenarios: autonomous agricultural machinery, coordinated UAV operations, variable-rate precision execution systems, picking/sorting robots, and intelligent facility agriculture. Through analyzing the deep integration paradigms of BeiDou navigation, multimodal perception, and deep learning technologies with agricultural operations, we demonstrate how this three-dimensional technology matrix spanning open fields to controlled environments, from ground-based to aerial systems, is reshaping the productivity landscape of modern agriculture [134].

3.1. Autonomous Driving Agricultural Machinery

Autonomous agricultural machinery demonstrates significant technical advantages and practical value in typical application scenarios [135]. For instance, the AV-3 autonomous tractor developed by Hokkaido University integrates RTK-GPS and a fiber optic gyroscope (FOG) to achieve high-precision navigation in farmlands, with lateral errors controlled within 6 cm, enabling versatile operations such as plowing and seeding [136]. In the realm of combine harvesters, the AV-8 (Yanmar AG1100) combines RTK-GPS with an inertial measurement unit (IMU), achieving a lateral error of merely 34.7 mm at an operational speed of 1.0 m/s. This system effectively adapts to complex terrains and enhances harvesting efficiency [137], as illustrated in Figure 10. For rice-planting scenarios, the AV-10 transplanter employs RTK-GNSS and a CAN bus communication system to ensure stable navigation in waterlogged paddy fields, with a maximum lateral error of 33.6 cm, significantly reducing manual operational demands. These cases highlight how autonomous driving technologies, through high-precision positioning and intelligent control algorithms, enable automation and precision across diverse agricultural machinery, offering viable solutions to labor shortages and improved agricultural productivity [138].

3.2. Drones and Aerial Operations

Unmanned aerial vehicles (UAVs) in agriculture are predominantly applied in two critical domains: precision crop protection and remote sensing monitoring [140]. In precision agrochemical application, UAVs equipped with multispectral sensors and intelligent spraying systems significantly enhance the efficiency of pesticide and fertilizer delivery. Studies indicate that UAV-enabled precision pesticide spraying reduces chemical usage by 40% [141], while liquid fertilizer application achieves 90–95% targeting accuracy, effectively minimizing nutrient loss and improving crop uptake efficiency. Furthermore, UAVs integrated with AI technologies enable precise weed zone identification, achieving 50–80% reduction in herbicide consumption through targeted spraying [142]. For remote sensing monitoring, UAVs utilizing multispectral and hyperspectral sensors (e.g., NDVI and GNDVI indices) facilitate the early detection of crop stress and diseases with up to 93% accuracy. Thermal imaging and microwave sensors have been successfully deployed for water stress analysis, as demonstrated in winter wheat and olive tree irrigation optimization, substantially enhancing water-use efficiency. These technologies, through real-time data acquisition and AI-driven analytics, deliver efficient and sustainable solutions for precision agriculture.

3.3. Precision Homework System

The core technological framework of agricultural automation precision operation systems is constructed through the synergistic integration of highly sophisticated hardware designs and intelligent algorithmic architectures [143]. At the hardware level, rigid robotic arms demonstrate exceptional performance in structured environments for crops like tomatoes and sweet peppers, achieving over 90% harvesting success rates due to their high payload capacity (up to 25 kg) and repetitive positioning accuracy (±0.1 mm). Their modular end-effectors enable rapid functional switching between cutting, gripping, and spraying operations, as depicted in Figure 11. Flexible robotic arms, utilizing silicone tendon actuators and shape-memory alloy materials, attain an 88% damage-free grasping rate in blackberry harvesting [144], with biomimetic structures adaptable to complex spatial distributions of grapevines (diameter < 8 mm). The deep integration of multimodal sensor arrays proves particularly critical. For instance, systems combining RGB-D cameras (±2 mm precision), LiDAR point clouds (0.1° resolution), and piezoelectric tactile sensors (0.1 N sensitivity) enable the real-time analysis of crop 3D pose, maturity, and biomechanical properties. This integration provides millimeter-level operational guidance for kiwifruit pollination and corn weeding through comprehensive environmental perception [145].

Breakthroughs in software systems are manifested through hierarchical innovations in algorithmic architectures. Vision perception modules based on improved SSD algorithms (25 fps detection speed) and Mask R-CNN (91.2% segmentation accuracy) effectively overcome interference from leaf occlusion (>50% coverage) and abrupt illumination changes (>2000 lux variations) [147]. In motion planning, hybrid algorithms integrating RRT* (40% improvement in path optimization rate) with model predictive control (MPC) enhance robotic arm trajectory smoothness by 32% and reduce energy consumption by 18% in vineyard pruning tasks. Digital twin technology enables the seamless transition from laboratory training to field deployment for blackberry harvesting robots through high-fidelity simulations (error < 3%), cutting testing costs by 67%. Mixed reality (MR) interfaces improve human–robot collaboration efficiency by 45%, with virtual safety boundaries (±5 cm accuracy) ensuring operational safety in dynamic environments. Despite persistent challenges such as multi-robot collaborative scheduling (response latency < 50 ms) and extreme weather adaptability (operational range of −20 °C to 50 °C), these technological innovations critically support the transformation of precision agriculture toward enhanced efficiency and intelligence [148].

3.4. Intelligent Greenhouses and Vertical Agriculture

The technological integration of smart greenhouses and vertical farming is driving agriculture toward high-precision and sustainable evolution [149]. Chen et al. [150] proposed a multi-layered vertical greenhouse architecture (Figure 12) equipped with mobile cameras and machine vision (NI Vision module) on customized rail systems, enabling real-time pest detection on leaf undersides. This system integrates organic pesticide atomization systems for targeted spraying, achieving minute-level response efficiency in pest control. Powered by an NI myRIO embedded platform for synchronized environmental parameter regulation, it combines PID and fuzzy control algorithms to excel in dynamic temperature–humidity balance conditions. Experimental data show that PID control stabilizes temperature errors within ±0.1 °C, while fuzzy control adapts to nonlinear environmental variations despite 3% RH humidity fluctuations. This design not only resolves uneven lighting distribution in traditional greenhouses, but also optimizes spatial utilization efficiency through rail-mounted camera scanning, increasing yield per unit area by over 40% [151,152].

Cepeda et al. [153] constructed a modular smart greenhouse from a sustainability perspective, employing a galvanized steel frame with UV-resistant polyethylene cladding. Integrated with a hydroponic nutrient circulation system (NFT technology) and central atomization devices, the system significantly reduced water and fertilizer consumption. Its Green Tech control module combines fuzzy logic, decision trees, and temporal control strategies, enabling multi-mode switching to address complex nonlinear environments. For instance, it automatically closes vents during strong winds or heavy rains to ensure structural safety while optimizing crop growth cycles through phased nutrient delivery. Both studies highlight the pivotal role of intelligent control systems: the Taiwanese solution achieves “targeted regulation” via machine vision and high-precision actuators, whereas the Mexican approach enhances system resilience through modular design and eco-friendly materials (e.g., biodegradable PVC piping). Their commonality lies in addressing labor shortages and climate risks through technological integration—vertical configurations and automated equipment minimize manual inspection needs, while intelligent algorithms and real-time data feedback (e.g., 1000 Hz SMA filtering in the Taiwanese study) improve adaptability to extreme weather. These innovations demonstrate that smart greenhouses and vertical farming represent not only productivity breakthroughs, but also exemplars of closed-loop resource management and climate-resilient agricultural practices [154].

4. Advantages and Benefits

4.1. Efficiency Enhancement

Agricultural automation significantly enhances operational efficiency through machine learning (ML) and deep learning (DL) technologies. For example, support vector machines (SVMs) and decision tree algorithms rapidly recommend suitable crops based on soil parameters (pH, nitrogen, phosphorus, potassium content, etc.), reducing the time and errors associated with manual judgment [111]. Extreme learning machines (ELMs), leveraging their rapid training capabilities, achieve high accuracy and low latency in crop classification tasks. Meanwhile, unmanned aerial vehicles (UAVs) integrated with ensemble learning techniques (e.g., random forests) streamline yield estimation, substantially shortening data acquisition cycles [155].

4.2. Resource Optimization

Precision resource management represents a core advantage of agricultural automation. Random forest regression models dynamically adjust irrigation volumes by analyzing soil moisture and meteorological data, minimizing water waste. Time-series analyses (e.g., ARIMA and RNN) predict pest and disease cycles, guiding targeted pesticide application to reduce environmental impacts from overuse. Additionally, fertilization optimization models based on soil nutrient data (nitrogen, phosphorus, potassium) curb fertilizer misuse and enhance resource utilization efficiency [156].

4.3. Economic Viability

Agricultural machinery automation delivers significant economic returns through reduced operational expenditures (OPEX), optimized resource utilization, and enhanced yield quality. Autonomous systems minimize labor dependency, addressing acute shortages in rural regions; for instance, autonomous tractors (e.g., Yanmar AG1100) reduce human operation costs by up to 40% while achieving sub-6 cm navigation precision, directly lowering replanting and overlap losses during seeding and harvesting [19]. UAV-based precision spraying curtails pesticide and fertilizer usage by 40–50%, translating to annual savings of USD 120–180/ha for staple crops like wheat and maize [141,142]. Solar-powered irrigation systems (SIPs) further cut energy expenditures, with Bangladesh reporting 20–30% lower irrigation costs compared to diesel pumps, achieving ROI within 3–5 years [126]. Smart greenhouses integrate AI-driven climate control to reduce energy consumption by 15–25% while stabilizing yields; PID-controlled systems maintaining ±0.1 °C temperature precision elevate premium crop output (e.g., tomatoes, berries) by 12–18%, enhancing market revenue [150]. Despite higher initial investment (e.g., modular robots cost 3–5× traditional equipment [92]), lifecycle cost analysis confirms that automation lowers long-term TCO (Total Cost of Ownership) by 22–35% through durability gains and resource efficiency. These economic advantages, amplified by policy incentives (e.g., India’s National Solar Mission subsidies [128]), position automation as a financially sustainable pathway for global farming.

4.4. Data-Driven Decision-Making

The integration of farmland big data and intelligent algorithms provides scientific foundations for decision-making. Internet of Things (IoT) sensors collect real-time soil, meteorological, and crop growth data, while ARIMA models forecast yield trends. Rajak et al. [157] developed an ensemble learning recommendation system (SVM+ANN) that delivers high-precision planting suggestions through soil data analysis. Similarly, Rajeswari [158] implemented an Alternating Decision Tree (ADT) model combined with GPS positioning to generate localized cultivation strategies for farmers, significantly improving profitability.

4.5. Addressing Labor Shortages

Automation technologies effectively mitigate rural labor shortages. Smart irrigation systems and UAV-based field inspections reduce reliance on manual labor, enabling elderly farmers to manage farmland efficiently [113]. Localized language interfaces (e.g., KNN-KD tree-based mobile apps) and simplified operational tools [159] lower technical barriers, empowering farmers with limited education to optimize production with advanced technologies, thereby counteracting labor migration impacts.

5. Challenges and Issues

5.1. Positioning Technologies

Agricultural machinery automation faces multiple challenges in positioning technologies. First, the diversity and complexity of agricultural environments impose stringent requirements on technological adaptability. For example, open fields rely on satellite navigation (e.g., GPS, BDS), but signals are prone to obstruction by tree canopies, greenhouse structures, or underwater environments, leading to increased positioning errors [17]. Orchards and facility agriculture necessitate visual or LiDAR-based local navigation, yet limitations such as lighting variations, terrain undulations, and sensor costs compromise stability and widespread adoption. Second, dynamic obstacle avoidance technologies remain underdeveloped, particularly in livestock and aquaculture settings, where the real-time reliability of sensor fusion algorithms requires improvement [160]. Additionally, while inertial navigation (IMU) offers short-term precision, error accumulation prevents its standalone use for prolonged operations. Multi-machine collaborative navigation, involving task allocation and path planning in complex farmland environments, remains immature and demands optimization through environmental perception and intelligent algorithms. Finally, the lag in high-precision, low-cost sensor development, coupled with compatibility issues in fragmented smallholder farming models, hinders technological dissemination. Although AI and big data are increasingly applied, challenges persist in environmental self-learning and multi-modal control integration.

5.2. Perception Technologies

Agricultural automation confronts significant challenges in image acquisition and processing. Monocular vision systems exhibit instability under complex lighting and background noise; for instance, citrus detection accuracy declines due to light reflections and adjacent fruit interference. Stereo vision systems provide 3D information but suffer from high costs and insufficient depth accuracy in intricate scenarios [161]. Hyperspectral imaging devices remain costly and data processing complexities restrict practical use. Image processing struggles with low-light conditions, noise, and low resolution, necessitating intensive preprocessing. Real-time segmentation accuracy for overlapping fruits or leaves remains suboptimal. In classification and algorithm applications, machine learning methods depend on extensive labeled data and exhibit limited generalization in dynamic environments. While deep learning models achieve high accuracy, their computational demands hinder real-time operations, and model optimization for diverse crops and environments is required. In practice, navigation and path planning face interference in complex terrains, and pesticide spraying systems lack stability under dynamic lighting and plant occlusion [162]. Moreover, high equipment costs impede adoption by small-scale farms, and reliance on technical expertise for system maintenance and operation creates barriers to farmer training and acceptance [163], collectively constraining large-scale implementation and efficacy.

5.3. Execution Technologies

Agricultural automation encounters multifaceted challenges in execution technologies. Modular robots, despite reconfigurability and adaptability, face barriers such as high costs, limited scalability, and integration issues with existing agricultural systems [164]. In control strategies, centralized systems risk single-point failures, while decentralized approaches demand intricate communication architectures. Environmental factors critically impact stability: drones experience 19% reduced flight time at wind speeds exceeding 10 mph, task failure rates reach 51% under high temperatures, and insufficient lighting causes image misjudgments at a rate of 50% in early operations [84]. Path planning algorithms like Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) perform well in dynamic settings but suffer from parameter tuning difficulties and local optima. Communication technologies (Wi-Fi, LoRa, LTE) enable large-scale coordination but face instability in remote areas with weak infrastructure, while RTK’s high cost limits adoption. Bio-inspired control strategies require real-time sensor feedback for decision optimization, and modular robots’ self-healing and shape-shifting capabilities need further validation for complex agricultural scenarios [92].

5.4. Artificial Intelligence Technologies

Agricultural automation faces critical challenges in AI applications [165]. First, farmers’ generally low educational levels hinder technology adoption, impeding dissemination. Second, agricultural data’s complexity and dynamism complicate feature selection and preprocessing, while incomplete datasets compromise model reliability. In time-series analysis, ARIMA models rely on linear assumptions and are constrained to short-term forecasting, whereas RNNs, though suitable for long-term predictions, demand substantial computational resources. Additionally, poor model interpretability (e.g., decision tree complexity) and localization needs (e.g., multilingual interfaces) limit practical implementation [104]. Finally, the immature integration of real-time data acquisition and IoT technologies fails to meet the dynamic requirements of precision agriculture.

5.5. Green Energy Technology

Agricultural automation confronts significant barriers in energy and power technologies. High initial and maintenance costs pose primary obstacles, particularly in developing countries [166]. Solar-powered irrigation systems reduce long-term operational expenses but face prohibitive upfront investments and battery storage costs. Renewable energy adoption is geographically constrained: wind energy requires stable wind speeds (7–9 m/s) and hydropower depends on seasonal water flow, leading to supply instability. Photovoltaic systems suffer performance degradation in agricultural settings due to dust accumulation, high-temperature-induced power loss (~−1.46% annually), and shading effects on crops, necessitating tailored module selection. Remote areas with inadequate grid coverage rely on decentralized renewable systems, which entail complex designs and demand technical training and spare part availability. Lastly, insufficient policy support, flawed subsidy mechanisms, and low public awareness of renewables delay technological adoption [167].

5.6. Synthesis of Key Cross-Cutting Challenges

Despite technological advancements, agricultural machinery automation faces persistent, interconnected challenges that impede widespread adoption. Environmental adaptability remains a fundamental constraint: dynamic field conditions (e.g., variable lighting, canopy occlusion, weather extremes) degrade the reliability of perception and positioning systems, causing navigation errors and operational failures in unstructured settings like orchards or greenhouses. Economic barriers, including high upfront costs for sensors, modular robotics, and green energy infrastructure, disproportionately affect smallholder farmers and developing regions, limiting scalability. Technical fragmentation exacerbates these issues—heterogeneous hardware/software standards hinder interoperability, while data scarcity and poor model generalizability restrict AI-driven decision-making. Additionally, energy sustainability gaps persist, with renewable solutions (e.g., solar, wind) constrained by geographic dependencies, storage inefficiencies, and performance degradation in agricultural environments. Resolving these challenges demands cohesive innovations in cost-effective edge computing, robust multi-modal sensor fusion, adaptive AI architectures, and policy-supported economic models to ensure equitable, sustainable deployment.

6. Future Development Trends

6.1. Intelligence and Autonomy

Future agricultural intelligence will deeply rely on advanced AI models and autonomous decision-making systems, driving the transition from traditional experience-driven practices to data-driven production [168]. Currently, support vector machines (SVMs), extreme learning machines (ELMs), and recurrent neural networks (RNNs) are widely applied in crop selection, yield prediction, and soil classification. With advancements in Transformer architectures and multimodal learning, AI models can integrate heterogeneous data sources (e.g., meteorological, soil, and imaging data) to significantly enhance predictive accuracy. For instance, ELM, leveraging its rapid learning capability and high generalization, is utilized for the real-time analysis of crop parameters (e.g., nitrogen, phosphorus, potassium content), while Transformer-based models show promise in addressing complex dependencies in long-sequence temporal data. The “unmanned farm” concept, enabled by fully autonomous machinery (e.g., intelligent seeders, driverless harvesters), achieves end-to-end automation in planting, monitoring, and harvesting [116]. Integrated with edge computing and real-time sensor networks, these systems minimize human intervention. Future advancements must address robustness challenges in complex environments, such as extreme weather disruptions to autonomous navigation [169].

6.2. Multi-Machine Collaboration and Swarm Operations

Agricultural automation will evolve toward multi-machine collaboration and swarm intelligence, optimizing resource utilization through coordinated UAVs, ground machinery, and robots. For example, UAVs equipped with hyperspectral imaging can monitor crop health in real time, generate precision fertilization maps, and synchronize with ground machinery for variable-rate irrigation [4]. Studies demonstrate that IoT-based collaborative systems enable low-latency data sharing among devices, ensuring synchronized and efficient task execution. Swarm robotic systems leverage distributed algorithms for dynamic task allocation, such as multi-robot weeding or pest control, offering flexibility in adapting to dynamic field conditions. Future efforts must optimize communication protocols and energy management to sustain large-scale swarm operations [170,171,172,173].

6.3. Sustainable Technologies

Carbon neutrality goals are accelerating the shift toward green and resource-efficient agricultural technologies. Machine learning models optimize water–fertilizer management strategies (e.g., random forest regression for irrigation demand prediction), significantly reducing resource waste. Electric agricultural machinery reduces carbon emissions from traditional fuel-powered equipment, while biofuels (e.g., straw-derived ethanol) and regenerative practices (e.g., crop rotation, cover cropping) enhance soil carbon sequestration. For example, stacked ensemble learning models integrate multi-source environmental data to predict crop water requirements accurately, minimizing groundwater depletion from over-irrigation [174,175]. Additionally, carbon footprint tracking integrated with blockchain quantifies the environmental impacts of agricultural activities, supporting carbon trading markets.

6.4. Integration of Emerging Technologies

Notably, 5G/6G communication and edge computing enable real-time data processing and decision-making. Field sensors transmit soil moisture, temperature, and other parameters to cloud platforms via low-latency networks, while edge nodes (e.g., FPGA-accelerated computing) perform localized rapid analysis [172]. Digital twin technology constructs virtual farm models to simulate the yield impacts of management strategies, exemplified by hybrid ARIMA-SVM models in corn cultivation. Blockchain enhances agricultural supply chain transparency, allowing consumers to verify production processes (e.g., pesticide usage records) via on-chain data. Future quantum computing may overcome computational bottlenecks in large-scale optimization tasks, such as field path planning [176,177,178,179].

6.5. Localization and Customization

Agricultural automation must balance global standards with regional needs to deliver tailored solutions. For instance, India’s GPS- and decision tree-based mobile app (DSIS) provides crop recommendations via local language interfaces, addressing accessibility barriers for low-literacy farmers. In Africa, lightweight RNN models enable short-term yield prediction on low-compute devices [121]. Future systems will require multilingual interfaces and adaptive algorithms, such as transfer learning to adapt temperate agriculture models to tropical environments [180,181,182,183]. International collaboration platforms (e.g., FAO data-sharing networks) will facilitate technology transfer and knowledge dissemination, promoting equitable global agricultural development [183,184,185,186].

7. Conclusions

Agricultural machinery automation represents a transformative paradigm for achieving the sustainable intensification of global agriculture. This review synthesizes significant advancements across core technological domains: high-precision positioning (e.g., RTK-GPS/LiDAR fusion enabling sub-6 cm navigation accuracy), intelligent perception (multispectral imaging and deep learning achieving >90% disease identification), adaptive control (modular robotics and bio-inspired algorithms optimizing swarm operations), AI-driven analytics (LSTM networks improving water-use efficiency by 27%), and renewable energy integration (solar microgrids reducing irrigation OPEX by 30%). These innovations underpin critical applications—autonomous tractors, UAV-mediated precision spraying (40% chemical reduction), variable-rate systems, and smart greenhouses (±0.1 °C climate control)—demonstrating quantifiable gains in productivity, resource efficiency (e.g., 15–25% energy savings), and labor substitution.

Notwithstanding these achievements, persistent challenges impede universal adoption. Technological heterogeneity creates scenario-specific limitations: satellite navigation falters under dense canopies, while visual systems exhibit illumination sensitivity. Economic barriers are pronounced, particularly for smallholders, as modular robots incur 3–5× higher costs than conventional equipment, and RTK-GPS infrastructure demands substantial investment. Dynamic environmental adaptability remains constrained, with UAV operational failure rates exceeding 50% under high temperatures or wind speeds >10 mph. Socio-technical disparities exacerbate adoption gaps in developing regions, where fragmented landholdings, limited technical training, and inadequate policy frameworks hinder implementation. Crucially, these challenges are interconnected: high costs amplify accessibility issues, while environmental variability complicates algorithm generalization.

Future progress necessitates interdisciplinary strategies targeting these bottlenecks. Technological convergence should prioritize lightweight edge computing for real-time processing in resource-limited settings, multi-modal sensor fusion (e.g., UWB–Visual–IMU hybrids) to overcome environmental obstructions, and self-repairing modular platforms enhancing operational resilience. Energy sustainability requires scalable multi-source systems integrating agrivoltaics, micro-wind, and hydropower with AI-optimized storage to ensure decarbonized operations. Policy mechanisms—including subsidized technology transfer, standardized data protocols, and carbon credit incentives—must bridge economic viability gaps, particularly in Global South contexts. Localized AI solutions, such as transfer learning adapting temperate crop models to tropical environments or multilingual farmer interfaces, will democratize access.

Ultimately, the evolution toward data-driven, autonomous, and networked agricultural systems holds profound implications for global sustainability. By systematically addressing the identified challenges through coordinated innovation in hardware, algorithms, policy, and cross-sector collaboration, agricultural automation can decisively contribute to SDG targets—notably zero hunger and responsible consumption—while ensuring equitable benefits across diverse agroecological and socioeconomic landscapes.