Search Results (604)

Protected horticulture moves fragile pots, plug trays, seedlings, harvested products, and carriers through narrow, humid, and crowded spaces. Transport robots must therefore integrate locomotion, perception, localization, handling, placement, scheduling, and human–robot interaction rather than operate as simple carts. This structured narrative review reorganizes evidence from seedling transplanting, nursery operations, harvest support, manipulation, perception, and autonomous navigation around the complete transport chain: target recognition, pickup, loading, loaded navigation, docking, unloading or placement, payload protection, and workflow feedback. The synthesis covers mobile platforms, payload support, perception and localization, motion control, gentle handling, digital support, and fleet coordination. Three barriers remain: short laboratory tests rarely provide season-long evidence; many prototypes are too specialized for variable workflows; and benchmarks seldom combine motion accuracy, handling reliability, payload quality, and resilience. Progress will require modular platforms, robust sensing, payload-safe control, standardized interfaces, and closer co-design between robotics and horticultural operations. Full article

Ball vegetables (such as cabbage, Chinese cabbage, broccoli, etc.) hold an important position in the vegetable industry due to their unique morphology and diverse applications and are widely favored by both consumers and the market. However, the harvesting of Ball vegetables poses significant challenges to agricultural production and market supply. Traditional manual harvesting struggles to meet the rapid demands of large-scale cultivation, primarily due to its high labor intensity and time-consuming nature, compounded by the increasingly prominent issues of aging and shortage of agricultural labor in recent years. As an alternative, intelligent harvesting robot technology, through integration with optimized cropping practices, innovations in preservation techniques, and improvements in processing workflows, offers an effective solution for expanding market planting areas and enhancing production efficiency. However, such harvesting robots still require further optimization and improvement in terms of adaptability, operational efficiency, and damage control. To systematically review the research progress and current status of this field, this study employs a bibliometric analysis approach to evaluate the current performance characteristics of various types of heading vegetable harvesting robots, aiming to provide a reference for future technological developments. This review analyzes solutions suitable for low-damage, high-quality harvesting of Ball vegetables in modern agriculture from five dimensions: identification and localization, row-following mechanisms, cutting mechanisms, pulling and conveying mechanisms, and leaf-removal mechanisms. It also summarizes the main challenges currently facing harvesting equipment, including the complexity of harvest targets, diversification of crop varieties and cultivation patterns, and harvest-induced damage to Ball vegetables. Finally, this review provides a future outlook on heading vegetable harvesting from four perspectives: research on the characteristics of Ball vegetables, investigation into harvest-induced damage mechanisms, improvement in machinery adaptability, and enhancement in equipment versatility and intelligence. Full article

The field inspection and ripeness detection of tomatoes in China remain heavily dependent on manual labor, while existing robotic solutions often exhibit limited functionality, poor environmental adaptability, prohibitive hardware costs, and unstable positioning accuracy. To address these limitations, this study proposes an intelligent tomato inspection robot that seamlessly integrates real-time ripeness recognition with precise spatial localization. Built upon a Raspberry Pi 5 core controller, the robot employs a lightweight, layered modular architecture designed to flexibly navigate complex agricultural environments. A comprehensive, multi-dimensional image dataset of tomato ripeness was constructed to train a three-category detection model based on the YOLOv8n architecture. Following 413 training epochs, the model demonstrated exceptional performance, achieving an overall mAP@0.5 of 87.8% and an mAP@0.5:0.95 of 72.7% on the held-out test dataset. In field inspections, the system achieved detection precisions of 82.22% for immature tomatoes, 92.66% for half-ripened tomatoes, and 100% for fully ripe tomatoes, successfully identifying all ripe tomatoes and satisfying the practical demands of field inspection. Furthermore, the integration of an Ultra-Wideband positioning system yielded an overall Root Mean Square Error of 0.231 m, successfully confining positioning errors to within 0.24 m to fully satisfy the stringent localization demands of crop-level inspection. Field evaluations confirmed that under optimal configurations, the robot can efficiently inspect a 50-m planting row in 10 min (±1 min) and maintains a continuous operational battery life of 2 h (±10 min). The core contribution of this work is the system-level integration and optimization of technologies for greenhouse agriculture. This integrated design achieves low hardware cost and high deployment flexibility, addressing longstanding challenges of labor-intensive inspection and delayed harvesting, and delivering a practical solution for intelligent tomato plantation management. Full article

Mechanized harvesting in the industrial tomato sector is currently bottlenecked by excessive mechanical injuries and elevated levels of foreign materials generated during electro-mechanical combine harvesting operations. To combat these limitations, this comprehensive review explores recent breakthroughs in harvester-mounted smart grading systems engineered specifically for complex, open-field conditions. Rather than relying solely on conventional optical inspection, the study examines the transition toward advanced, heterogeneous edge-computing frameworks—incorporating FPGAs and embedded GPUs—deployed within electro-mechanical harvesting platforms. This architectural evolution plays a crucial role in mitigating unpredictable processing delays caused by intense operational vibrations, although achieving absolute real-time stability under extreme field conditions remains an ongoing challenge. To minimize bruising and physical deterioration, our analysis synthesizes findings from multi-scale explicit dynamic finite element simulations, unpacking the underlying microstructural failure modes of the crop. We illustrate how regulating applied forces via soft robotic effectors can help approach a ‘damage-free’ handling threshold, though empirical results vary depending on fruit maturity and dynamic operational speeds. Furthermore, coupling multi-modal sensor fusion with Convolutional Neural Networks (CNNs) shows promising potential for non-destructive internal property evaluation under the vibration, dust, and throughput constraints of electro-mechanical harvesters, pending broader validation across diverse field datasets. Ultimately, by projecting future trends in onboard electro-mechanical harvester separation and advocating for a closer synergy between agronomic practices and machine engineering, this paper delivers a comprehensive blueprint for building next-generation, highly resilient, and gentle sorting machinery. Full article

To improve grape target perception and picking-point positioning for intelligent harvesting robots, this study develops a vision-based method for orchard grape detection and harvesting-point localization. The method is intended to address missed detections, insufficient recognition accuracy, and unsatisfactory peduncle segmentation caused by illumination variation, occlusion, and interference from branches and leaves in complex orchard scenes. For grape cluster and peduncle detection, a lightweight YOLOv7-derived model, termed YOLO-FES, was established. In this model, FasterNet and SCConv were introduced to refine the backbone and neck structures, and the EMA mechanism was incorporated to lower parameter complexity and computational cost while improving detection performance. For suspended grape structure association and peduncle extraction, the GJK algorithm was combined with nearest-neighbor rectangular discrimination, and an improved YOLACT-based peduncle segmentation network, named M-YOLACT, was constructed. With the integration of the MLCA mechanism and the Mish activation function, accurate peduncle segmentation was achieved. In addition, a stereo depth camera was employed to obtain two-dimensional picking-point information and further recover the corresponding three-dimensional spatial coordinates. Experimental results showed that the mAP@0.5 of YOLO-FES for grape clusters and peduncles reached 95.37%. For grape peduncle segmentation, the mAP@0.5 values of the bounding boxes and masks produced by M-YOLACT reached 95.73% and 94.36%, respectively. The proposed method achieved an overall harvesting success rate of 89.2%, with an average time consumption of 11 s for a single harvesting operation. By integrating deep-learning-based detection and segmentation with binocular-vision localization, this study provides a practical technical solution and useful reference for the visual system design of grape-harvesting robots. Full article

Artificial intelligence is transforming agriculture from a mechanized, labor-intensive sector into a data-driven, perception-enabled, and increasingly autonomous production system. In this review, AI serves as an umbrella term encompassing machine learning, computer vision, and robotic control, among other technologies. We synthesize recent advances across the tillage–sowing–management–harvesting (TSMH) workflow, covering intelligent tillage, precision sowing, field management, and robotic harvesting. The literature shows that AI has significantly improved agricultural perception, prediction, and task-level decision-making. However, large-scale adoption remains constrained by data heterogeneity, limited cross-scene generalization, environmental uncertainty, and insufficient integration across operational stages. Future progress will depend on multimodal data fusion, lightweight and interpretable models, cloud-edge collaboration, and full-chain decision architectures. By framing current research within the TSMH pipeline, this review highlights both technical advances and the critical bottlenecks that must be addressed to move smart agriculture from stage-specific intelligence toward system-level autonomy. Representative studies indicate that AI models can improve soil-property prediction and reduce sowing miss-detection rates to below 3% under controlled or bench-top conditions. However, field deployment may be affected by environmental variability, including illumination changes, dust, vibration, occlusion, and hardware constraints. These limitations highlight the need for robust and edge-compatible architectures. Full article

Tree-climbing robots are primarily utilized for pruning and harvesting in tall trees; however, limited structural degrees of freedom (DoFs) reduce their flexibility in complex environments. To improve the flexibility and environmental adaptability of the robots, this study proposes a novel three-armed claw-type tree-climbing robot inspired by gibbons. A 14 DoFs prototype with a total mass of approximately 2.52 kg was developed, comprising three manipulator arms and independently actuated claws. Kinematic models were separately established for the series-connected arms and the parallel-connected moving platform, with accuracy verified through numerical simulations. Based on these models, a control system was implemented, and a physical prototype was tested in field climbing experiments. Grasping tests on surfaces of varying roughness, including moist tree trunks, artificial wood, and smooth steel plates, demonstrated the adaptability of the claw to diverse materials. The robot successfully climbed trunks inclined at 52–90°, supporting a maximum payload of 1.81 kg; each full gait cycle averaged approximately 4 min. These results indicate that the robot can successfully imitate the movements of gibbons during climbing, thereby verifying the feasibility and practical application value of this bionic design in real-world forestry environments. Full article

Printable piezoelectric materials are emerging as a cornerstone of next-generation sensing, actuation, and energy harvesting technologies, driven by the need for lightweight, flexible, and digitally manufactured transducers. Conventional ceramic piezoelectrics offer exceptional electromechanical performance but require high-temperature sintering and exhibit intrinsic brittleness, limiting their integration with soft or unconventional substrates. Polymeric piezoelectrics, in contrast, provide mechanical compliance and low-temperature processability yet suffer from lower crystallinity, reduced piezoelectric coefficients, and limited thermal stability. These contrasting characteristics have catalyzed the development of functional piezoelectric inks—ceramic, polymeric, and hybrid formulations engineered for additive manufacturing techniques such as direct ink writing, stereolithography, screen printing, and inkjet printing. This review systematically examines the material compositions, dispersion chemistries, printing requirements, thermal treatment pathways, and poling strategies that govern the performance of printed piezoelectric transducers. By comparing ceramic-based, polymer-based, and hybrid systems, we reveal the fundamental trade-offs between printability, crystallinity, mechanical compliance, and electromechanical response, and map how these trade-offs shape device design across wearable electronics, soft robotics, and structural health monitoring. Finally, we highlight emerging approaches—including surface functionalization, low-temperature crystallization, liquid-phase sintering, and engineered ceramic–polymer interfaces—that offer promising routes to bridge the gap between printability and high piezoelectric performance. Full article

Accurate tomato ripeness detection is crucial for automated harvesting; however, complex greenhouse environments—characterized by dynamic light interference, foliage occlusion, and dense fruit overlapping—severely hinder detection performance and lead to frequent misdetections. This study aims to develop a high-precision, lightweight detection model that simultaneously addresses these three core challenges, thereby providing a technically deployable algorithmic foundation for resource-constrained agricultural edge devices. To this end, we propose CFD-DETR, a lightweight tomato ripeness detection model based on the RT-DETR architecture. The model incorporates a CAEfficientViT backbone for the lightweight extraction of multi-scale color and texture features. Furthermore, a Focused Efficient Additive Attention (FEAA) mechanism is integrated to capture fine-grained local ripening traits with minimal computational overhead. During feature reconstruction, a Deep Dynamic Upsampling (DwDySample) operator is utilized to preserve semantic integrity. Additionally, we designed the Wise-SIoU loss function, which dynamically penalizes low-quality samples to enhance boundary fitting and robustness against background noise. Experimental evaluations demonstrate that CFD-DETR achieves 90.2% mAP@0.5, outperforming the baseline model by 2.1 percentage points while significantly reducing the parameter count and computational complexity by 47.2% and 52.5%, respectively. Cross-dataset validation on the publicly available Laboro Tomato and RaUTD datasets confirms the model’s superior generalization capabilities. Overall, CFD-DETR provides a highly efficient and robust solution for real-time agricultural robotics. Full article

Energy consumption is a critical concern for Internet of Things (IoT) platforms lacking abundant resources, particularly for swarm robotic systems that rely on numerous devices operating collaboratively over extended periods. This study presents a comprehensive design strategy for improving processing and communication to enhance system efficiency and reduce energy consumption. We incorporate energy harvesting (photovoltaic and RF), dynamic power management, and energy-efficient communication protocols (e.g., duty cycle, power control, data compression) into two complementary platforms built for swarm robotics: MCU-based nodes (TI MSP430 with LoRa transceiver), which serve as the experimental prototype for validating energy-aware communication, compression, and scheduling mechanisms; edge platforms (Jetson Nano and TX2), which are used for high-level power profiling and system-level evaluation, particularly for computation intensive workloads and comparative analysis. Our technique involves analyzing the device’s energy usage and harvesting processes, developing efficient communication protocols, and validating the system through simulations and hardware prototypes. Experimental results under outdoor and indoor conditions show that the device maintains an energy neutrality ratio well above unity, even with limited ambient energy. Key findings include significant reductions in energy per bit transmitted and reliable long-term operation. These insights pave the way for deploying swarms of autonomous IoT-based robots with minimal maintenance and maximal longevity. Full article

Self-oscillators convert constant external stimuli into sustained mechanical work, offering potential for applications such as soft robotics, energy absorption, and mechanical logic. However, the effective design of a light-driven self-oscillation system is challenging due to geometrically constrained deformation modes and the inherent rigidity of rectilinear light propagation paths. Notably, the mirror-reflected optical feedback loop decouples the feedback mechanism from geometric constraints imposed by deformation modes, enabling dynamic coupling independent of structural geometry. In this study, we introduce a geometry-flexible light feedback loop to drive a liquid crystal elastomer (LCE) self-oscillator. The system comprises an optically responsive LCE fiber, a spring, a mirror, and a perforated plate. By integrating the dynamic photon propagation path in light feedback routing with the dynamic deformation model of the LCE, we develop a dynamic theoretical model of the oscillator under constant illumination. Numerical simulations reveal two distinct patterns: static equilibrium and self-oscillation. Self-oscillation is generated by the light-induced contraction of LCE fiber segments illuminated by reflected light. Crucially, mirror-reflected light enables localized deformations anywhere along the fiber to contribute to global displacement feedback, thereby transcending the constraints of geometric deformation modes. This capability transcends the limitations posed by constrained geometric deformation modes, enabling adaptable control of the optical feedback loop through simple geometric alterations. This innovative approach circumvents the need for intricate structural feedback designs and separate energy harvesters, as well as actuator systems. Full article

Artificial intelligence (AI) is transforming modern agriculture from experience-driven practices to data-driven production paradigms. To provide an in-depth analysis of AI technologies in intelligent agriculture, we retrieved literature from Web of Science, IEEE Xplore, Google Scholar and Scopus, covering publications from 2015 to 2025, and 85 articles remained after screening 1867 relevant publications. These articles are grouped into three stages from perception, to decision making, to execution (PDE) in a closed-loop framework. At the perception level, we highlight progress in intelligent sensing systems, such as unmanned aerial vehicle (UAV) and multi-modal monitoring platforms, for crop disease and pest detection, growth monitoring and abiotic stress assessment. At the decision making level, integration of heterogeneous data sources, including meteorological records, soil measurements, remote sensing (RS) imagery and market information, supports advanced analytics, such as yield prediction, pest and disease warning, irrigation and fertilization planning, and crop management optimization. At the execution level, agricultural robots equipped with simultaneous localization and mapping (SLAM) and deep reinforcement learning (RL) facilitate precision spraying, autonomous harvesting, and unmanned field operations. Overall, AI technologies demonstrate substantial potential in the PDE pipeline of agricultural production. However, several challenges remain, including heterogeneous data fusion, limited generalization across diverse environments, complex system integration, and high hardware and deployment costs. Future directions are discussed from the perspectives of lightweight model design, cross-platform standardization, enhanced human–machine collaboration, and a deeper integration of emerging AI paradigms to support scalable, robust, and autonomous agricultural intelligence systems. Full article

To explore robotic peach picking in different modes, this study examined the effects of various peach picking modes on harvesting force and time. A finite element model of peach harvesting structure was established, and harvesting experiment parameters were based on the Box–Behnken design. Harvesting was simulated to collect response time and force data. Subsequently, the optimal harvesting rate under different picking modes was determined. Different picking modes were tested by simulating identical fruit harvesting in the laboratory at the optimal harvesting speed to determine the peak harvesting force and duration. The Bend mode had the lowest picking pressure and the shortest average picking time at 0.7 MPa and 4.2 s, respectively. The Pull and Twist modes had similar pressures and picking times at 1.2 and 1.1 MPa and 5.2 and 5.6 s, respectively. Harvesting in the orchard allowed for harvesting force and duration measurement under different picking modes. The differences in picking pressure and time among the three picking modes increased compared with those of simulated picking, with specific patterns being observed. Picking pressure appeared at P1max, and differences in picking time were prevalent during separation. This study offers valuable insights for future improvements in harvesting modes and efficiency enhancement. Full article

The selective harvesting of leaves from flexible-stem crops remains a major challenge in agricultural mechanization due to stem compliance, heterogeneous petiole strength, and unstable tool–crop interaction. To address these issues, a bio-inspired ring-cutting and compliant clamping harvesting mechanism is proposed for low-damage selective harvesting under complex terrain conditions. Inspired by the adaptive attachment behavior of octopus suckers, a flexible compliant clamping interface combined with a ring-shaped sliding cutting structure was developed to stabilize flexible stems during harvesting. A coupled kinematic–force analytical model was established to characterize the interaction between tool motion, stem feeding, and cutting behavior. In addition, a sliding cutting mechanics model was introduced to analyze the relationship between cutting force and sliding angle. Dynamic multibody simulations were performed using ADAMS to verify the motion feasibility and trajectory stability of the proposed harvesting mechanism. Bench-scale experiments were conducted using mulberry branches as a representative flexible-stem crop, and a response surface methodology based on a Box–Behnken experimental design was applied to optimize key operational parameters. The optimal parameter combination included a chain linear speed of 0.18 m·s⁻¹, a feeding speed of 0.30 m·s⁻¹, and an installation angle of 36°. Under these conditions, the missed harvest rate was reduced to 9.2–9.8%, demonstrating improved harvesting stability compared with conventional rigid cutting mechanisms. The results indicate that integrating compliant stabilization with sliding cutting provides an effective engineering strategy for selective harvesting of flexible-stem crops in complex agricultural environments. Full article

Automated fruit harvesting is crucial for alleviating labor shortages and enhancing agricultural productivity. In this context, it is crucial to obtain information on fruit poses before picking in order to avoid damaging the fruit and/or the plant. However, the complex and unstructured orchard environment poses significant challenges regarding the pose estimation task. In this study, a dragon fruit pose estimation (DFPE) framework using a single RGB image is proposed for dragon fruit automated harvesting, which includes three key components: dataset annotation processing, keypoint detection, and geometric pose estimation. First, a multi-source dataset consisting of 8467 images is constructed to enhance the estimation model’s generalizability. A pseudo four-keypoint annotation strategy is designed to fit the annotation rules of mainstream single-class keypoint detection models and mitigate the inherent limitations of multi-target keypoint detection in agricultural scenarios. This strategy implicitly encodes the fruit’s orientation using bounding box group IDs, while preserving geometric information for pose inference. Then, the fruit body and its two core keypoints (navel and stem) are detected via a real-time keypoint detection model. Notably, the proposed DFPE framework is detector-agnostic: other mainstream keypoint detection models can also be plugged into the subsequent geometric pose inference stage, which guarantees the generality and scalability of the framework. Finally, a dragon fruit pose estimation algorithm based on customized geometric constraints is designed, which takes the detected pose information as the input and outputs the posture of dragon fruit. The results of experiments conducted in natural orchard and laboratory environments demonstrate that the ellipses fitted using the proposed DFPE framework closely aligned with fruit contours, even under foliage occlusion conditions. In the laboratory environment, roll errors reached a maximum of 14.8°, whereas yaw errors peaked at 13.4°. Crucially, all roll and yaw errors remained consistently below 15°, which is well within the tolerance threshold required for non-destructive picking operations using a harvesting robot. In summary, this work presents a low-cost solution for dragon fruit pose estimation from a single RGB image, which can potentially be extended to other ellipsoid crops and is suitable for implementation in harvesting robots operating in orchards. Full article