Search Results (347)

Global labour shortages in the agricultural sector, combined with diminishing arable land and a growing population, are driving investment in autonomous agricultural machinery. Autonomous systems that can navigate crop environments and perform planting, treatment, and harvesting alongside humans are required, but the gap between published research and commercially deployed systems remains wide across most operational scenarios. Why are agricultural robots still not widely deployed in real farms despite decades of research in autonomous navigation and path planning, and what is preventing full farm autonomy? This paper reviews the principal enabling technologies for autonomous agricultural integration, with a specific focus on path planning as the differentiator between research-stage and deployed systems. Current research in human–robot integration, open-field navigation, row identification and following, crop sensing, and power efficiency is synthesised and evaluated against a deployability criterion. A Deployability Assessment Framework is introduced, comprising structured tables that assign Technology Readiness Levels to twelve path-planning families and benchmark eleven commercial and research platforms against field-validated accuracy data. The analysis shows that point-to-point GNSS navigation has reached TRL 9 with over one million commercial units deployed, vision-based crop row following is at TRL 5–7 depending on crop and season, and whole-farm autonomy with dynamic re-planning is at TRL 3–5. The primary barriers are the absence of standardised evaluation benchmarks, the failure of perception models to generalise across seasons and crop types, and the decoupling of terrain and slip feedback from global path planners. Our review reveals that open-field GNSS navigation is commercially mature, but true whole-farm agricultural autonomy remains unsolved because current systems are not robust enough across seasons, terrain, sensing conditions, and operational transitions. Full article

The modern agricultural sector faces a multidimensional crisis, mainly consisting of an aging workforce, labor shortages, exhausting working conditions, and high rates of work-related accidents. In response, the authors carried out a systematic literature review (SLR), to explore the latest research on smart agricultural technologies, their effects on occupational safety, ergonomics, and worker health, and pinpoint obstacles to sustainable adoption. A thorough search was performed solely in the Scopus database, covering peer-reviewed publications from 2020 to 2026, strictly following the PRISMA 2020 guidelines. Based solely on Scopus, this study provides a focused synthesis, with the results suggesting that hazards such as chemical exposure and musculoskeletal strain are significantly reduced with the use of innovations such as unmanned vehicles, exoskeletons, and collaborative robots. These technologies also show great promise in cutting down resource waste, helping farmers practice sustainable agriculture. However, a recurring gap between research and real-life deployment exists, as adoption is hindered by cost considerations, reliability issues, and ergonomic problems. To achieve a sustainable technological transition in agriculture, it is necessary to simultaneously bridge three critical gaps: technological (ensuring robust field performance), ergonomic (design and testing processes based on real end-users and their needs), and socio-economic (addressing adoption barriers). Full article

Weeds create a significant challenge in agricultural production by competing with crops for essential resources such as nutrients, sunlight, and water. This competition leads to reduced crop yields and quality, resulting in substantial economic losses. Consequently, there is a critical need for effective weed control strategies to mitigate the impact of unwanted plant growth and ensure sustainable agricultural practices. In precision agriculture, enabling autonomous navigation between crop rows during tasks such as weeding and harvesting presents a significant research challenge, particularly when leveraging cost-effective technological solutions. Effective robot navigation requires adaptive traffic management strategies and robust object recognition capabilities to distinguish between cultivated and uncultivated areas. In fact, the integration of computer vision techniques into these systems is essential for optimizing trafficability in cropping fields and enhancing robots’ dynamics for better working efficiency in agricultural environments. This review addresses the challenge of enhancing inter-row navigation in field crops and delivering reliable guidance for autonomous agricultural robots. A PRISMA-based systematic review methodology was adopted to identify, screen, and analyze 38 relevant studies selected from the Scopus and Web of Science databases. The selected studies are classified according to their target platform (Unmanned Ground Vehicles and Unmanned Aerial Vehicles) and grouped into three methodological categories: conventional computer vision, deep learning architectures, and hybrid approaches. The findings provide practical guidance for selecting appropriate vision-based crop row detection technologies according to the application requirements and highlight key research directions toward more robust, cost-effective, and adaptable autonomous navigation systems. This article presents an outline of artificial-intelligence-based row detection methods used in agricultural fields and a classification of related semantic segmentation approaches. Unlike previous surveys, it provides an overview of the technological progress in agricultural robots and navigation based on systems vision for crop row detection, with a focus on comparisons balancing technical performance with economic and practical constraints. Full article

Orchard production involves intensive labor requirements, limited operational periods, and highly dynamic and complex working environments. Consequently, the development of intelligent orchard robots has become a important approach to enhancing production efficiency and reducing reliance on manual operations. This review focuses on the demands of autonomous robotic systems operating in challenging orchard scenarios and provides a comprehensive overview of key technologies, including environmental perception and semantic cognition, autonomous navigation and environmental modeling, intelligent task execution, and collaborative robotic systems. Recent advances in fruit and blossom detection, branch and canopy structure perception, multi-modal sensor fusion for localization, semantic mapping, robotic harvesting control, variable-rate spraying, precision pollination, and autonomous intra-row weed management are systematically discussed. Furthermore, emerging technologies such as multi-robot coordination, robot–UAV cooperation, large language models (LLMs), and vision-language models (VLMs) for enhancing decision-making capabilities in agricultural robotics are reviewed. Finally, the existing challenges of orchard robots in terms of perception reliability, long-term autonomous navigation, operational robustness, system-level integration, and standardized performance evaluation are analyzed, followed by discussions on potential future research directions. Full article

This scoping review synthesizes 35 studies on sensor technologies and artificial intelligence for fruit detection, classification, and quality assessment, contrasting Latin American and international research traditions. The analysis suggests notable differences in technological approaches between the included Latin American and international studies: Latin American research tends to emphasize in developing accessible, practical solutions using classical computer vision and low-cost hardware, while international studies more frequently employ through deep learning architectures, multi-modal sensing, and complete robotic automation systems. Across the included studies, relatively limited attention was given to AI-assisted decision support for agricultural practitioners, insufficient consideration of inclusivity, and the scarce integration of environmental sustainability into intelligent sensing system design. The review identifies that only 8 of 35 studies originate from Latin America, suggesting an uneven geographical distribution of the available evidence. The included studies generally reported high accuracy values, yet these findings must be interpreted with caution given the reliance on curated datasets that may not represent real-world variability. The reviewed evidence suggests that future research may benefit not from one approach dominating the other, but from a thoughtful integration of complementary strategies, including knowledge transfer, edge computing democratization, and human-centered design. Overall, this review suggests that the ultimate goal extends beyond accuracy metrics to the transformation of agricultural practices that enhance food security, economic development, and environmental sustainability across the global agricultural landscape. It is important to note that this work does not propose or validate a new fruit detection algorithm but rather synthesizes and critically evaluates existing scientific evidence regarding sensor technologies and artificial intelligence applied to fruit detection and quality assessment. Full article

Binocular stereo vision is a low-cost and scalable 3D perception technology that shows strong potential in agricultural phenotyping and smart agriculture. By estimating depth from multi-view RGB images, it enables non-contact, high-precision sensing of crop structure, canopy morphology, growth dynamics, and livestock traits, providing essential support for digital and intelligent agricultural production. With recent advances in deep learning-based stereo matching, multimodal sensor fusion, and 3D reconstruction, its robustness and accuracy in complex field environments have been significantly improved. This paper systematically reviews recent progress in agricultural applications of binocular stereo vision, covering system architectures, traditional and deep learning-based stereo matching methods, point cloud reconstruction techniques, and emerging supervision strategies such as 3D Gaussian splatting. It further summarizes key applications, including high-throughput phenotyping, fruit localization and robotic harvesting, weed detection and precision spraying, autonomous navigation, and livestock body condition assessment, highlighting its role in multi-task agricultural perception systems. Finally, the paper discusses major challenges, including low-texture matching difficulty, occlusions in complex environments, cross-domain generalization, real-time lightweight deployment, and limited dataset availability. Future directions are outlined in foundation model-based visual perception, self- and weakly supervised learning, multimodal fusion, and edge-efficient model design, aiming to support large-scale deployment in smart agriculture. Full article

Swarm control technology, leveraging artificial intelligence algorithms to coordinate multiple devices, offers an effective solution for developing precision and intelligent operation systems in orchard management. This paper focuses on the core technologies underpinning swarm coordination and reviews the current state of research on collaborative communication, path planning, task allocation, and formation control, with reference to both domestic and international studies. Based on the full growth cycle of fruit trees, encompassing monitoring, precision management, and harvesting, the paper summarizes the applications and research progress of swarm control technology at each stage. Furthermore, it identifies key challenges in applying swarm technology to orchard environments, including low efficiency in heterogeneous system coordination, delayed responses to dynamic conditions, resource constraints in large-scale swarm systems, and limited adaptability to agricultural contexts, and offers strategic recommendations to address these limitations. Full article

The transition toward Agriculture 4.0 aims to improve farm performance and sustainability; however, existing macroeconomic indicators do not fully capture the depth of farm-level digital adoption. This study proposes the Digital Farm Index (DFI) as a tool for assessing digital maturity and regional disparities in European agriculture. The research combines bibliometric mapping of the scientific literature with an empirical DFI assessment for 18 European Union Member States using Eurostat data. The empirical assessment utilizes multiple linear regression, log-linear scale modeling, and hierarchical clustering to analyze adoption patterns and structural determinants. The index integrates four dimensions: connectivity, precision agriculture, robotics, and farm management information systems (FMIS). Results indicate that adoption is concentrated in larger farms, as area-weighted digital maturity (DFI-Hectares) consistently exceeds farm-level adoption (DFI-Farms). CAPEX-based cost modeling suggests the existence of a technological indivisibility threshold, whereby digitalization may become an entry barrier for fragmented farms with limited economies of scale. Multiple linear regression suggests an East–West structural trend: while farm size influences the territorial diffusion of technology, a more consolidated regional innovation ecosystem appears more relevant for farm-level adoption. Findings also highlight a hardware–software imbalance and limited use of data-driven managerial tools. Support policies should therefore move beyond equipment subsidies and include technology transfer networks, digital skills, and managerial data-integration tools. Full article

This review presents an up-to-date summary of AI impacts on energy utilization in a broad spectrum of modern robotic platforms, solutions in the scope of AI employment in different robotics areas, and the crucial role AI plays in energy requirements of robotic stations. Notably, among the multitude of references devoted to robotics, energy, and AI technologies, most of the “early AI” approaches are either excluded from examination or serve as auxiliary components of the full-fledged AI robotic systems. Instead, this research reveals solutions for stationary and mobile robotics, the competent application of which can make the greatest impact on energy savings. The work reveals two interconnected directions contributing to reducing losses and improving energy recovery. The first one concerns platforms for AI-assistive process-level design and programming of robotic cells. The second way covers so-called AI robots, whose behavior is not only fully controlled by AI but also results in an obvious energy-saving effect. Thus, the study demonstrates how AI can provide robot management, thereby unlocking their potential to decline energy consumption in industry, agriculture, transport, household and other areas of vital activity. Full article

Agriculture 5.0 represents a breakthrough transformation from Agriculture 4.0, addressing the needs for safety, sustainability, and resilience in human–robot collaboration in agriculture. By integrating technologies such as artificial intelligence, digital twins, and big data, it achieves breakthroughs in three areas: perception, cognition, and execution. As carriers of agricultural technology, agricultural automated guided vehicles (AGVs) are an indispensable part of agricultural activities and play a crucial role in agricultural production. In the actual operation of AGVs, intelligent perception is a core technological prerequisite for enabling the communication and interaction among machines, humans, and the environment. However, due to the complexity and variability of agricultural environments, intelligent perception technology remains a highly challenging task. While existing reviews have focused on isolated aspects of agricultural automation, a comprehensive synthesis of intelligent perception technologies for agricultural AGVs within the holistic, human-centric framework of Agriculture 5.0 is notably lacking. This review bridges this gap by systematically analyzing and comprehensively reviewing recent advances in intelligent perception for agricultural AGVs, covering multi-sensor technologies, visual perception and target recognition, positioning and navigation algorithms, as well as applications such as path planning, multi-robot coordination, and human–robot collaboration. Furthermore, this paper delves into the potential challenges and future development trends of intelligent perception technology in the context of Agriculture 5.0, highlighting the transformative potential of these technologies in promoting multimodal fusion and addressing safety issues in human–robot collaboration. In the context of Agriculture 5.0, with the continuous advancement of intelligent perception technologies and the ongoing improvement of agricultural intelligent equipment, a human-centered, highly sustainable, resilient, data-driven agricultural production system will ultimately be formed in the future. Full article

Path tracking control and algorithm portability for agricultural robots serve as the core technological foundation for achieving precision and automation in farming operations, playing a critical role in ensuring food security and enhancing production efficiency. This paper systematically reviews recent technological advancements in the field. It first elucidates the fundamental theories and technical components of path tracking control, providing detailed analyses of the characteristics and limitations of traditional methods such as Proportional-Integral-Derivative (PID) control, model predictive control (MPC), sliding-mode control (SMC), and the Stanley algorithm. Subsequently, it focuses on innovations in intelligent technologies, exploring the integration trends of adaptive control and intelligent learning algorithms, with particular emphasis on the combined applications of reinforcement learning, deep learning, and intelligent control methodologies. The paper clarifies the significance of algorithm portability and summarizes the current applications and performance differences among various algorithms. The study concludes that traditional methods demonstrate stability and reliability in structured scenarios, while advanced intelligent approaches exhibit stronger adaptability in complex environments, albeit facing challenges such as data dependency and real-time deployment requirements. Future technological developments will prioritize deep integration of multiple technologies and the unified achievement of both safety and real-time performance. Full article

A virtual training environment offers clear advantages for agricultural robotics. It provides a safe setting in which perception, navigation, and control algorithms can be evaluated without risking damage to either the robot or the crop. It also supports efficient data generation: large volumes of training data can be collected under diverse environmental conditions that would be costly, slow, and often season-dependent in real-world deployments. This broader variability improves model adaptability, reduces the risk of overfitting, and leads to more robust operation. In this paper, we argue that digital twin technology should therefore be understood not merely as a passive mirror of a physical robot, but as an active training environment in which multiple sensor-related subprocesses can be developed, tested, validated, and refined jointly. This paper is based on our experiences with digital twin technology used in the development of a vineyard robot, including a self-driving rover, sensor simulation, procedural map generation, and agriculture-specific movement models. Our contribution is threefold: we reinterpret the digital twin as a training space, propose a layered framework for training agricultural robots in virtual environments, and explain why agriculture is a particularly strong use case, given variable field conditions, expensive real-world experimentation, and persistent labor scarcity. To validate this framework, we present the simulation-based evaluation of an autonomous reinforcement learning agent. The agent has been trained entirely in this virtual environment, which successfully navigated to 155 out of 161 target points in a simulated vineyard demonstration environment. Full article

Italian “Agricultural Engineering”, while evolving toward the broader, interdisciplinary field of “Biosystems Engineering” (which also includes the study of biomasses/biomaterials, field and forest mechanization in difficult contexts and advanced post-harvest agri-food technologies), suffers from a structural critical issue due to its historical academic placement within the Agricultural rather than the Engineering departments. This positioning limits the depth of the technical subjects proposed to the students and does not facilitate the necessary collaboration with core engineering disciplines in research and didactics activities, thereby potentially slowing innovation in crucial fields like agro-bio-energies, precision agriculture and field robotics. To address this misalignment and foster inter-departmental synergy, this study proposes adopting the Contamination Lab (C-Lab) model as the archetype of a possible framework of academic and professional networking involving and centered on Agricultural Engineering. C-Labs (transdisciplinary platforms proposed by the Italian Ministry of University and Research) function as experiential laboratories, gathering students from Engineering, Agronomy, Computer Science, and Economics to collaboratively develop solutions to real-world challenges posed by industry stakeholders. The integration of a permanent, thematic C-Lab focused on agri-forestry and food machinery, supported by methodologies for enhancing creativity in technical fields, such as design thinking, represents an effective (and necessary) strategy to give Agricultural Engineering greater visibility in the Italian (and international) scenario and, prospectively, relocate it to the center of any research involving the technological and technical aspects of agriculture, forestry and food production. It is concluded that this initiative can serve as an institutional bridge for hybrid training, which is essential for aligning academic competencies with the growing demands for innovation and multidisciplinary professionalism in the national agri-food tech sector. Full article

Digital technologies provide effective tools for formulating sustainable, evidence-based policies; however, this field has so far lacked a cohesive and practical framework to guide their application. Providing comprehensive answers to six primary research questions, this study aims to address this critical gap concerning the prerequisites, challenges, opportunities, key technologies, policy areas, and critical success factors (CSFs) for applying digital technologies in environmental sustainability policymaking. In this study, 39 articles were analyzed from 293 documents indexed in the Web of Science as of 19 August 2025, in accordance with the PRISMA 2020 guidelines. The prerequisites are categorized into the following themes: fiscal incentives, a culture of innovation and sustainability, effective regulations, robust digital infrastructures, participation, and reliable and accessible data. We identified significant challenges, including financial constraints, human resource deficits, infrastructural and regulatory gaps, and the adverse environmental impacts of digital technologies themselves. Opportunities emerged under two main domains: effective policymaking and enhanced environmental management. Our study indicates that pioneering technologies at the core of this transformation include artificial intelligence, big data, blockchain, the Internet of Things, machine learning, and robots. Their applications are predominant in key policy areas, including the environment, energy, climate change, urban sustainability, agriculture, industry, and food security. The analysis identifies four CSFs: the policy–digital–sustainability nexus, fundamental processes, soft capacities, and hard capacities. Full article

This paper introduces the research progress of path planning, trajectory tracking control, and multi-machine collaborative operation systems for agricultural robots. It summarizes the development laws of 3D terrain modeling and adaptive path planning algorithms for complex agricultural environments such as hills and mountains, and analyzes the dynamic disturbance characteristics of agricultural machinery under slip, sideslip, and dynamic load changes. Through comprehensive analysis, it is found that traditional kinematic control models have limitations in complex and unstructured environments. Combining soil mechanics mechanisms, variable load identification, and robust control strategies is key to improving trajectory tracking stability and operational quality. In terms of multi-machine collaboration, this paper discusses master–slave collaboration, distributed control, and task allocation modes. It further identifies that the stability of collaboration and interoperability standards between devices in weak network environments are currently the main bottlenecks limiting the large-scale application of this technology. Finally, this paper provides prospects for future research directions and suggests strengthening the closed-loop integration of perception, decision-making, and dynamic models, establishing industry unified standards, and enhancing the safety of the entire lifecycle of operations, providing suggestions for the unmanned application of agricultural robots. Full article