Search Results (765)

The commercialization of selective broccoli harvesters, a critical response to agricultural labor shortages, is hampered by end effectors with large operational envelopes and poor adaptability to complex field conditions. To address these limitations, this study developed and evaluated a novel end-effector with an integrated transverse cutting mechanism and a foldable grasping cavity. Unlike conventional fixed cylindrical cavities, our design utilizes actuated grasping arms and a mechanical linkage system to significantly reduce the operational footprint and enhance maneuverability. Key design parameters were optimized based on broccoli morphological data and experimental measurements of the maximum stem cutting force. Furthermore, dynamic simulations were employed to validate the operational trajectory and ensure interference-free motion. Field tests demonstrated an operational success rate of 93.33% and a cutting success rate of 92.86%. The end effector successfully operated in dense planting environments, effectively avoiding interference with adjacent broccoli heads. This research provides a robust and promising solution that advances the automation of broccoli harvesting, paving the way for the commercial adoption of robotic harvesting technologies. Full article

We propose a data-efficient framework for automating sowing operations by agricultural robots in densely mixed polyculture environments. This study addresses the challenge of enabling robots to identify suitable sowing positions with minimal labeled data by integrating image-based field sensing with expert agricultural knowledge. We collected 84 RGB-depth images from seven field sites, labeled by synecological farming practitioners of varying proficiency levels, and trained a regression model to estimate optimal sowing positions and seeding quantities. The model’s predictions were comparable to those of intermediate-to-advanced practitioners across diverse field conditions. To implement this estimation in practice, we mounted a Kinect v2 sensor on a robot arm and integrated its 3D spatial data with axis-specific movement control. We then applied a trajectory optimization algorithm based on the traveling salesman problem to generate efficient sowing paths. Simulated trials incorporating both computation and robotic control times showed that our method reduced sowing operation time by 51% compared to random planning. These findings highlight the potential of interpretable, low-data machine learning models for rapid adaptation to complex agroecological systems and demonstrate a practical approach to combining structured human expertise with sensor-based automation in biodiverse farming environments. Full article

Robotics has emerged as a transformative discipline at the intersection of the engineering, computer science, and cognitive sciences. This state-of-the-art review explores the current trends, methodologies, and challenges in both robotics research and education. This paper presents a comprehensive review of the evolution of robotics, tracing its development from early automation to intelligent, autonomous systems. Key enabling technologies, such as Artificial Intelligence (AI), soft robotics, the Internet of Things (IoT), and swarm intelligence, are examined along with real-world applications in healthcare, manufacturing, agriculture, and sustainable smart cities. A central focus is placed on robotics education, where hands-on, interdisciplinary learning is reshaping curricula from K–12 to postgraduate levels. This paper analyzes instructional models including project-based learning, laboratory work, capstone design courses, and robotics competitions, highlighting their effectiveness in developing both technical and creative competencies. Widely adopted platforms such as the Robot Operating System (ROS) are briefly discussed in the context of their educational value and real-world alignment. Through case studies, institutional insights, and synthesis of academic and industry practices, this review underscores the vital role of robotics education in fostering innovation, systems thinking, and workforce readiness. The paper concludes by identifying the key challenges and future directions to guide researchers, educators, industry stakeholders, and policymakers in advancing robotics as both technological and educational frontiers. Full article

Accurate weed detection in soybean fields is essential for enhancing crop yield and reducing herbicide usage. This study proposes a YOLO-SW model, an improved version of YOLOv8, to address the challenges of detecting weeds that are highly similar to the background in natural environments. The research stands out for its novel integration of three key advancements: the Swin Transformer backbone, which leverages local window self-attention to achieve linear O(N) computational complexity for efficient global context capture; the CARAFE dynamic upsampling operator, which enhances small target localization through context-aware kernel generation; and the RTDETR encoder, which enables end-to-end detection via IoU-aware query selection, eliminating the need for complex post-processing. Additionally, a dataset of six common soybean weeds was expanded to 12,500 images through simulated fog, rain, and snow augmentation, effectively resolving data imbalance and boosting model robustness. The experimental results highlight both the technical superiority and practical relevance: YOLO-SW achieves 92.3% mAP@50 (3.8% higher than YOLOv8), with recognition accuracy and recall improvements of 4.2% and 3.9% respectively. Critically, on the NVIDIA Jetson AGX Orin platform, it delivers a real-time inference speed of 59 FPS, making it suitable for seamless deployment on intelligent weeding robots. This low-power, high-precision solution not only bridges the gap between deep learning and precision agriculture but also enables targeted herbicide application, directly contributing to sustainable farming practices and environmental protection. Full article

Background: Drones are rapidly establishing themselves as one of the most critical technologies. Robotics, automated machinery, intelligent manufacturing, and other high-impact technological research and applications bring up pressing ethical, social, legal, and political issues. Methods: The present research aims to present the results of a qualitative investigation that looked at perceptions of the growing socio-ethical conundrums surrounding the development of drone applications. Results: According to the obtained results, participants often share similar opinions about whether different drone applications are approved by the public, regardless of their level of experience. Perceptions of drone applications appear consistent across various levels of expertise. The most notable associations are with military objectives (73%), civil protection (61%), and passenger transit and medical purposes (56%). Applications that have received high approval include science (8.70), agriculture (8.78), and disaster management (8.87), most likely due to their obvious social benefits and reduced likelihood of ethical challenges. Conclusions: The study’s findings can help shape the debate on drone acceptability in particular contexts, inform future research on promoting value-sensitive development in society more broadly, and guide researchers and decision-makers on the use of drones, as people’s attitudes, understanding, and usage will undoubtedly impact future advancements in this technology. Full article

Recent years have seen significant interest among agricultural researchers in using robotics and machine vision to enhance intelligent orchard harvesting efficiency. This study proposes an improved hybrid framework integrating YOLO VX deep learning, 3D object recognition, and SLAM-based navigation for harvesting ripe fruits in greenhouse environments, achieving servo control of robotic arms with flexible end-effectors. The method comprises three key components: First, a fruit sample database containing varying maturity levels and morphological features is established, interfaced with an optimized YOLO VX model for target fruit identification. Second, a 3D camera acquires the target fruit’s spatial position and orientation data in real time, and these data are stored in the collaborative robot’s microcontroller. Finally, employing binocular calibration and triangulation, the SLAM navigation module guides the robotic arm to the designated picking location via unobstructed target positioning. Comprehensive comparative experiments between the improved YOLO v12n model and earlier versions were conducted to validate its performance. The results demonstrate that the optimized model surpasses traditional recognition and harvesting methods, offering superior target fruit identification response (minimum 30.9ms) and significantly higher accuracy (91.14%). Full article

Smart farming is an agricultural technology integrating advanced technology such as cloud computing, Artificial Intelligence (AI), the Internet of Things (IoT), and robots into traditional farming. Smart farming can help farmers by increasing agricultural production and managing resources efficiently. However, malicious attackers can attempt security attacks because communication in smart farming is conducted via public channels. Therefore, an authentication scheme is necessary to ensure security in smart farming. In 2024, Rahaman et al. proposed a privacy-centric authentication scheme for smart farm monitoring. However, we demonstrated that their scheme is vulnerable to stolen mobile device, impersonation, and ephemeral secret leakage attacks. This paper suggests a secure and privacy-preserving scheme to resolve the security defects of the scheme proposed by Rahaman et al. We also verified the security of our scheme through “the Burrows-Abadi-Needham (BAN) logic”, “Real-or-Random (RoR) model”, and “Automated Validation of Internet Security Protocols and Application (AVISPA) tool”. Furthermore, a performance analysis of the proposed scheme compared with related studies was conducted. The comparison result proves that our scheme was more efficient and secure than related studies in the smart farming environment. Full article

Strawberry (Fragaria × ananassa), a nutrient-dense fruit with significant economic value in commercial cultivation, faces critical detection challenges in automated harvesting due to complex growth conditions such as foliage occlusion and variable illumination. To address these limitations, this study proposes Ripe-Detection, a novel lightweight object detection framework integrating three key innovations: a PEDblock detection head architecture with depth-adaptive feature learning capability, an ADown downsampling method for enhanced detail perception with reduced computational overhead, and BiFPN-based hierarchical feature fusion with learnable weighting mechanisms. Developed using a purpose-built dataset of 1021 annotated strawberry images (Fragaria × ananassa ‘Red Face’ and ‘Sachinoka’ varieties) from Changchun Xiaohongmao Plantation and augmented through targeted strategies to enhance model robustness, the framework demonstrates superior performance over existing lightweight detectors, achieving mAP50 improvements of 13.0%, 9.2%, and 3.9% against YOLOv7-tiny, YOLOv10n, and YOLOv11n, respectively. Remarkably, the architecture attains 96.4% mAP50 with only 1.3M parameters (57% reduction from baseline) and 4.4 GFLOPs (46% lower computation), simultaneously enhancing accuracy while significantly reducing resource requirements, thereby providing a robust technical foundation for automated ripeness assessment and precision harvesting in agricultural robotics. Full article

With the advancement of agricultural automation technologies, apple-harvesting robots have gradually become a focus of research. As their “perceptual core,” machine vision systems directly determine picking success rates and operational efficiency. However, existing vision systems still exhibit significant shortcomings in target detection and positioning accuracy in complex orchard environments (e.g., uneven illumination, foliage occlusion, and fruit overlap), which hinders practical applications. This study proposes a visual system for apple-harvesting robots based on improved Mask R-CNN and binocular vision to achieve more precise fruit positioning. The binocular camera (ZED2i) carried by the robot acquires dual-channel apple images. An improved Mask R-CNN is employed to implement instance segmentation of apple targets in binocular images, followed by a template-matching algorithm with parallel epipolar constraints for stereo matching. Four pairs of feature points from corresponding apples in binocular images are selected to calculate disparity and depth. Experimental results demonstrate average coefficients of variation and positioning accuracy of 5.09% and 99.61%, respectively, in binocular positioning. During harvesting operations with a self-designed apple-picking robot, the single-image processing time was 0.36 s, the average single harvesting cycle duration reached 7.7 s, and the comprehensive harvesting success rate achieved 94.3%. This work presents a novel high-precision visual positioning method for apple-harvesting robots. Full article

In practical applications, the utilization of multi-robot systems (MRS) is extensive and spans various domains such as search and rescue operations, mining operations, agricultural tasks, and warehouse management. The surge in demand for MRS has prompted extensive exploration of Multi-Robot Task Allocation (MRTA). Researchers have devised a range of methodologies to tackle MRTA problems, aiming to achieve optimal solutions, yet there remains room for further enhancements in this field. Among the complex challenges in MRTA, the identification of an optimal coalition formation (CF) solution stands out as one of the (Nondeterministic Polynomial) NP-hard problems. CF pertains to the effective coordination and grouping of agents or robots for efficient task execution, achieved through optimal task allocation. In this context, this paper delivers a succinct overview of dynamic task allocation and CF strategies. It conducts a comprehensive examination of diverse strategies employed for MRTA. The analysis encompasses the advantages, disadvantages, and comparative assessments of these strategies with a focus on CF. Furthermore, this study introduces a novel classification system for prominent task allocation methods and compares these methods with simulation analysis. The fidelity and effectiveness of the proposed CF approach are substantiated through comparative assessments and simulation studies. Full article

The continuous and reliable operation of autonomous systems is important for farm management decision making, whether such systems perform crop monitoring using imaging systems or crop handling in pruning and harvesting applications using robotic manipulators. Autonomous systems, including robotic ground vehicles, drones, and tractors, are major research efforts of precision crop management. However, these systems may be less effective or require specific customizations for planting systems in low tunnels, high tunnels, or other environmentally controlled enclosures. In this work, a compact and lightweight crop management platform is developed that uses overhead power cables for continuous operation over row crops, requiring less human intervention and independent of the ground terrain conditions. The platform does not carry batteries onboard for its operation, but rather pulls power from overhead cables, which it also uses to navigate over crop rows. It is developed to be modular, with the top section consisting of mobility and power delivery and the bottom section addressing a custom task, such as incorporating additional sensors for crop monitoring or manipulators for crop handling. This prototype illustrates the infrastructure, locomotive mechanism, and sample usage of the system (crop imaging) in the application of low-tunnel-covered strawberries; however, there is potential for other row crop systems with regularly spaced support structures to adopt this platform as well. Full article

Strawberry harvesting represents a labor-intensive agricultural operation where existing end-effector pose control algorithms frequently exhibit insufficient precision in fruit grasping, often resulting in unintended damage to target fruits. Concurrently, deep learning-based pose control algorithms suffer from inherent training instability, slow convergence rates, and inefficient learning processes in complex environments characterized by high-density fruit clusters and occluded picking scenarios. To address these challenges, this paper proposes an enhanced reinforcement learning framework DRFW-TQC that integrates Dynamic L2 Regularization for adaptive model stabilization and a Group-Wise Feature Weighting Network for discriminative feature representation. The methodology further incorporates a picking posture traction mechanism to optimize end-effector orientation control. The experimental results demonstrate the superior performance of DRFW-TQC compared to the baseline. The proposed approach achieves a 16.0% higher picking success rate and a 20.3% reduction in angular error with four target strawberries. Most notably, the framework’s transfer strategy effectively addresses the efficiency challenge in complex environments, maintaining an 89.1% success rate in eight-strawberry while reducing the timeout count by 60.2% compared to non-adaptive methods. These results confirm that DRFW-TQC successfully resolves the tripartite challenge of operational precision, training stability, and environmental adaptability in robotic fruit harvesting systems. Full article

Accurate estimation of the leaf area index (LAI), a key indicator of canopy development and light interception, is essential for improving productivity in greenhouse tomato cultivation. This study presents a non-destructive LAI estimation method using side-view images captured by a vertical scanning system. The system recorded the full vertical profile of tomato plants grown under two deleafing strategies: modifying leaf height (LH) and altering leaf density (LD). Vegetative and leaf areas were extracted using color-based masking and semantic segmentation with the Segment Anything Model (SAM), a general-purpose deep learning tool. Regression models based on leaf or all vegetative pixel counts showed strong correlations with destructively measured LAI, particularly under LH conditions (R² > 0.85; mean absolute percentage error ≈ 16%). Under LD conditions, accuracy was slightly lower due to occlusion and leaf orientation. Compared with prior 3D-based methods, the proposed 2D approach achieved comparable accuracy while maintaining low cost and a labor-efficient design. However, the system has not been tested in real production, and its generalizability across cultivars, environments, and growth stages remains unverified. This proof-of-concept study highlights the potential of side-view imaging for LAI monitoring and calls for further validation and integration of leaf count estimation. Full article

This study investigates the operational performance of fruit-picking robots under varying terrain slopes and soil moisture conditions, with a focus on comparing wheeled and tracked locomotion systems. A modular robot platform was designed and tested in both controlled environments and actual mountainous orchards in Shandong, China. The experiments assessed key performance metrics—average speed, slip rate, and path deviation—under combinations of four slope levels (0°, 8°, 18°, 28°) and three soil moisture levels (dry 10%, moderate 20%, wet 35%). Results reveal that wheeled robots perform optimally on dry and flat terrain but experience significant slippage and path deviation under steep and wet conditions. In contrast, tracked robots maintain better stability and terrain adaptability, demonstrating lower slip rates and more consistent trajectories across a wide range of conditions. A synergistic deterioration effect was observed when high slope and high soil moisture co-occur, significantly degrading the performance of wheeled systems, while tracked systems mitigated these effects. Complementary semi-structured interviews with 20 orchard stakeholders—including farmers, growers, and hired pickers—highlighted key user expectations: robust traction, terrain adaptability, reduced physical labor, and operational safety. The findings suggest that future agricultural robots should adopt adaptive hybrid mobility systems and integrate environmental perception capabilities to enhance performance in complex agricultural scenarios. These insights contribute practical and theoretical guidance for the design and deployment of intelligent fruit-picking robots in diverse field environments. Full article

The experimentation of agricultural robots has been increasing in recent years, both in greenhouses and open fields. While agricultural robots are inherently useful for automating various farming tasks, their presence can also be leveraged to collect measurements along their paths. This approach enables the creation of a complete and detailed picture of the climate conditions inside a greenhouse, reducing the need to distribute a large number of physical devices among the crops. In this regard, choosing the best visiting sequence of the Points of Interest (PoIs) regarding where to perform the measurements deserves particular attention. This trajectory planning has to carefully combine the amount and significance of the collected data with the energy requirements of the robot. In this paper, we propose a method based on Deep Reinforcement Learning enriched with a Proximal Policy Optimization (PPO) algorithm for determining the best trajectory an agricultural robot must follow to balance the number of measurements and autonomy adequately. The proposed approach has been applied to a real-world case study regarding a greenhouse in Verona (Italy) and compared with other existing state-of-the-art approaches. Full article