Search Results (21)

Autonomous field robots are increasingly used in precision agriculture for monitoring, phenotyping, spraying, and site-specific intervention, yet reliable autonomy remains difficult under vegetation occlusion, uneven terrain, variable illumination, and intermittent communications. This review provides a deployment-oriented synthesis of navigation and sensor-fusion methods for agricultural robots, with emphasis on what is practical under field conditions rather than only in laboratory settings. The literature was examined through a structured narrative-review workflow using Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, and related citation tracking, with primary emphasis on studies published between 2015 and 2025. We compare global, local, and hybrid planning methods; motion-control strategies such as PID, Pure Pursuit, and MPC; and localization pipelines that combine GNSS, IMU, LiDAR, cameras, odometry, and SLAM or Kalman-family fusion. Beyond algorithm summaries, the review links method selection to agricultural deployment constraints, including GNSS degradation, dynamic obstacles, compute limits, ROS 2 integration, time synchronization, and coordinate-frame management. The synthesis shows that no single stack is optimal across all crop systems: lightweight GNSS/IMU-based solutions remain attractive in structured open fields, whereas orchards, vineyards, and other occluded environments benefit more from tighter multi-sensor fusion and SLAM-supported localization. Finally, the review distills design guidance for sensing, planning, validation, and digital-twin-supported testing, and identifies research gaps related to robustness, benchmarking, safety, and scalable on-farm deployment. 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

Weed management in woody perennial crops relies mainly on broadcast herbicide application, with well-documented costs to soil health, biodiversity, and crop physiology. Robotic platforms offer a path toward selective, site-specific control, but orchard environments present challenges such as irregular geometries, trunks, and strong illumination variability under the canopy that have limited fully integrated solutions. This work presents an autonomous robotic platform for selective under-canopy weed control in woody crops, combining multi-sensor perception, a six-degree-of-freedom robotic arm with a mechanical trunk-avoidance mechanism, and a precision spraying module with independently controlled nozzles. A weed image dataset tailored to Mediterranean orchard conditions was built from controlled-cultivation and commercial-orchard imagery under a two-phase training strategy, and the platform was evaluated in a commercial almond orchard in southern Spain. Field trials confirmed the platform’s ability to avoid tree trunks (presenting an average of 1.28% coverage near the tree trunks) and spray only selected targets under typical orchard operation but weed detection accuracy dropped substantially between winter conditions (mAP@0.5 = 93.5%) and summer conditions (mAP@0.5 = 47.8%), with uneven canopy lighting identified as the main cause. These results confirm the technical feasibility of integrating perception, navigation, and actuation into a single autonomous platform, while highlighting robust perception under canopy-induced illumination heterogeneity and tighter perception–navigation integration as the main remaining challenges. Full article

Autonomous mobile robots are playing an increasingly significant role in modern smart orchards by supporting precision agricultural operations such as target-oriented spraying and autonomous harvesting. Nevertheless, achieving high-precision trajectory tracking and stable motion in complex, unstructured orchard environments remains challenging, because tracking deviations induced by uneven terrain and low-traction soil can directly affect operational safety and efficiency. To address this challenge, the present study proposes an adaptive tracking controller which integrates model-driven and data-driven approaches. Firstly, a six-state planar dynamic model based on Newton–Euler equations is established to describe motion characteristics. Secondly, an improved Particle Swarm Optimization (PSO) algorithm is employed for offline parameter optimization under representative operating conditions. The process thus engenders a mapping dataset that relates the real-time motion states of the orchard mobile robot to the optimized horizon parameters and weights. Finally, an Adaptive Neuro-Fuzzy Inference System (ANFIS) is trained using this dataset, enabling adaptive adjustment of MPC parameters according to the robot motion state. Simulation and experimental results demonstrate that, in Double-Lane-Change (DLC) and serpentine simulations, the proposed controller reduced lateral and heading Root-Mean-Square (RMS) errors to 0.0109 m/0.0081 rad and 0.0102 m/0.0117 rad, achieving reductions of 49.30–85.58% and 68.60–88.02% compared with Pure Pursuit, Stanley, Linear Quadratic Regulator (LQR), and traditional MPC, respectively. In orchard field tests with circular and Figure-8 trajectories at 0.3–0.6 m/s, the lateral RMS errors were recorded as 0.0112–0.0182 m and 0.0156–0.0262 m, respectively, corresponding to reductions of 46.94–61.52% relative to traditional MPC, while the heading RMS error remained below 0.0510 rad. These findings substantiate the efficacy of the proposed controller in enhancing the accuracy and adaptability of the system, thereby providing a resilient and precise control framework for operation within orchard environments. Full article

To address the challenges of low pesticide utilization, insufficient automation, and health risks in orchard plant protection, we developed an autonomous spraying vehicle using ArduPilot firmware and a robot operating system (ROS). The system tackles orchard navigation hurdles, including global navigation satellite system (GNSS) signal obstruction, light detection and ranging (LIDAR) simultaneous localization and mapping (SLAM) error accumulation, and lighting-limited visual positioning. A key innovation is the integration of an extended Kalman filter (EKF) to dynamically fuse T265 visual odometry, inertial measurement unit (IMU), and GPS data, overcoming single-sensor limitations and enhancing positioning robustness in complex environments. Additionally, the study optimizes PID controller derivative parameters for tracked chassis, improving acceleration/deceleration control smoothness. The system, composed of Pixhawk 4, Raspberry Pi 4B, Silan S2L LIDAR, T265 visual odometry, and a Quectel EC200A 4G module, enables autonomous path planning, real-time obstacle avoidance, and multi-mission navigation. Indoor/outdoor tests and field experiments in Sun Village Orchard validated its autonomous cruising and obstacle avoidance capabilities under real-world orchard conditions, demonstrating feasibility for intelligent plant protection. Full article

Agricultural robots can mitigate labor shortages and advance precision farming. However, the dense vegetation canopies and uneven terrain in orchard environments reduce the reliability of traditional GPS-based localization, thereby reducing navigation accuracy and making autonomous navigation challenging. Moreover, inefficient path planning and an increased risk of collisions affect the robot’s ability to perform tasks such as fruit harvesting, spraying, and monitoring. To address these limitations, this study integrated stereo visual odometry with real-time appearance-based mapping (RTAB-Map)-based simultaneous localization and mapping (SLAM) to improve mapping and localization in both indoor and outdoor orchard settings. The proposed system leverages stereo image pairs for precise depth estimation while utilizing RTAB-Map’s graph-based SLAM framework with loop-closure detection to ensure global map consistency. In addition, an incorporated inertial measurement unit (IMU) enhances pose estimation, thereby improving localization accuracy. Substantial improvements in both mapping and localization performance over the traditional approach were demonstrated, with an average error of 0.018 m against the ground truth for outdoor mapping and a consistent average error of 0.03 m for indoor trails with a 20.7% reduction in visual odometry trajectory deviation compared to traditional methods. Localization performance remained robust across diverse conditions, with a low RMSE of 0.207 m. Our approach provides critical insights into developing more reliable autonomous navigation systems for agricultural robots. Full article

In order to guide orchard management robots to realize some tasks in orchard production such as autonomic navigation and precision spraying, this research proposed a deep-learning network called dynamic fusion segmentation network (DFSNet). The network contains a local feature aggregation (LFA) layer and a dynamic fusion segmentation architecture. The LFA layer uses the positional encoders for initial transforming embedding, and progressively aggregates local patterns via the multi-stage hierarchy. The fusion segmentation module (Fus-Seg) can format point tags by learning a multi-embedding space, and the generated tags can further mine the point cloud features. At the experimental stage, significant segmentation results of the DFSNet were demonstrated on the dataset of orchard fields, achieving an accuracy rate of 89.43% and an mIoU rate of 74.05%. DFSNet outperforms other semantic segmentation networks, such as PointNet, PointNet++, D-PointNet++, DGCNN, and Point-NN, with improved accuracies over them by 11.73%, 3.76%, 2.36%, and 2.74%, respectively, and improved mIoUs over the these networks by 28.19%, 9.89%, 6.33%, 9.89, and 24.69%, respectively, on the all-scale dataset (simple-scale dataset + complex-scale dataset). The proposed DFSNet can capture more information from orchard scene point clouds and provide more accurate point cloud segmentation results, which are beneficial to the management of orchards. Full article

The accurate and rapid acquisition of fruit tree canopy parameters is fundamental for achieving precision operations in orchard robotics, including accurate spraying and precise fertilization. In response to the issue of inaccurate citrus tree canopy segmentation in complex orchard backgrounds, this paper proposes an improved DeepLabv3+ model for fruit tree canopy segmentation, facilitating canopy parameter calculation. The model takes the RGB-D (Red, Green, Blue, Depth) image segmented canopy foreground as input, introducing Dilated Spatial Convolution in Atrous Spatial Pyramid Pooling to reduce computational load and integrating Convolutional Block Attention Module and Coordinate Attention for enhanced edge feature extraction. MobileNetV3-Small is utilized as the backbone network, making the model suitable for embedded platforms. A citrus tree canopy image dataset was collected from two orchards in distinct regions. Data from Orchard A was divided into training, validation, and test set A, while data from Orchard B was designated as test set B, collectively employed for model training and testing. The model achieves a detection speed of 32.69 FPS on Jetson Xavier NX, which is six times faster than the traditional DeepLabv3+. On test set A, the mIoU is 95.62%, and on test set B, the mIoU is 92.29%, showing a 1.12% improvement over the traditional DeepLabv3+. These results demonstrate the outstanding performance of the improved DeepLabv3+ model in segmenting fruit tree canopies under different conditions, thus enabling precise spraying by orchard spraying robots. Full article

Crop row detection is one of the foundational and pivotal technologies of agricultural robots and autonomous vehicles for navigation, guidance, path planning, and automated farming in row crop fields. However, due to a complex and dynamic agricultural environment, crop row detection remains a challenging task. The surrounding background, such as weeds, trees, and stones, can interfere with crop appearance and increase the difficulty of detection. The detection accuracy of crop rows is also impacted by different growth stages, environmental conditions, curves, and occlusion. Therefore, appropriate sensors and multiple adaptable models are required to achieve high-precision crop row detection. This paper presents a comprehensive review of the methods and applications related to crop row detection for agricultural machinery navigation. Particular attention has been paid to the sensors and systems used for crop row detection to improve their perception and detection capabilities. The advantages and disadvantages of current mainstream crop row detection methods, including various traditional methods and deep learning frameworks, are also discussed and summarized. Additionally, the applications for different crop row detection tasks, including irrigation, harvesting, weeding, and spraying, in various agricultural scenarios, such as dryland, the paddy field, orchard, and greenhouse, are reported. Full article

Traditional Japanese orchards control the growth height of fruit trees for the convenience of farmers, which is unfavorable to the operation of medium- and large-sized machinery. A compact, safe, and stable spraying system could offer a solution for orchard automation. Due to the complex orchard environment, the dense tree canopy not only obstructs the GNSS signal but also has effects due to low light, which may impact the recognition of objects by ordinary RGB cameras. To overcome these disadvantages, this study selected LiDAR as a single sensor to achieve a prototype robot navigation system. In this study, density-based spatial clustering of applications with noise (DBSCAN) and K-means and random sample consensus (RANSAC) machine learning algorithms were used to plan the robot navigation path in a facilitated artificial-tree-based orchard system. Pure pursuit tracking and an incremental proportional–integral–derivative (PID) strategy were used to calculate the vehicle steering angle. In field tests on a concrete road, grass field, and a facilitated artificial-tree-based orchard, as indicated by the test data results for several formations of left turns and right turns separately, the position root mean square error (RMSE) of this vehicle was as follows: on the concrete road, the right turn was 12.0 cm and the left turn was 11.6 cm, on grass, the right turn was 12.6 cm and the left turn was 15.5 cm, and in the facilitated artificial-tree-based orchard, the right turn was 13.8 cm and the left turn was 11.4 cm. The vehicle was able to calculate the path in real time based on the position of the objects, operate safely, and complete the task of pesticide spraying. Full article

Orchard spraying robots must visually obtain citrus tree crown growth information to meet the variable growth-stage-based spraying requirements. However, the complex environments and growth characteristics of fruit trees affect the accuracy of crown segmentation. Therefore, we propose a feature-map-based squeeze-and-excitation UNet++ (MSEU) region-based convolutional neural network (R-CNN) citrus tree crown segmentation method that intakes red–green–blue-depth (RGB-D) images that are pixel aligned and visual distance-adjusted to eliminate noise. Our MSEU R-CNN achieves accurate crown segmentation using squeeze-and-excitation (SE) and UNet++. To fully fuse the feature map information, the SE block correlates image features and recalibrates their channel weights, and the UNet++ semantic segmentation branch replaces the original mask structure to maximize the interconnectivity between feature layers, achieving a near-real time detection speed of 5 fps. Its bounding box (bbox) and segmentation (seg) AP50 scores are 96.6 and 96.2%, respectively, and the bbox average recall and F1-score are 73.0 and 69.4%, which are 3.4, 2.4, 4.9, and 3.5% higher than the original model, respectively. Compared with bbox instant segmentation (BoxInst) and conditional convolutional frameworks (CondInst), the MSEU R-CNN provides better seg accuracy and speed than the previous-best Mask R-CNN. These results provide the means to accurately employ autonomous spraying robots. Full article

To solve the problem encountered when the spraying robot has run out of medicine even though the spraying task on the field is not complete, we developed a spraying–dosing robot group and proposed a collaborative navigation system based on an orchard map. Firstly, we constructed a 3D orchard point cloud map and set up navigation path points on the projected map. Secondly, we developed a master–slave command-based cooperative navigation strategy, where the spraying robot was the master and the dosing robot was the slave. Finally, the spraying robot and the dosing robot completed the cooperative navigation on the constructed map by using the pure pursuit algorithm and D-A control algorithm, respectively. To validate the cooperative navigation system, we conducted field tests on the separate communication and navigation control. The results of communication experiments demonstrated that the packet loss rate was less than 5%, which satisfied communication requirements. The experimental results of the navigation control demonstrated that the maximum value of the absolute lateral error is 24.9 cm for the spraying robot and 29.7 cm for the dosing robot. The collaborative navigation system proposed in this research can meet the automatic navigation requirements of the spraying–dosing robot group for collaborative tasks in traditional orchards. Full article

The precise path-tracking control of tractors and trailers is the key to realizing agricultural automation. In order to improve the path-tracking control accuracy and driving stability of orchard traction spraying robots, this study proposed a navigation path-tracking control algorithm based on Double Deep Q-Network (Double DQN). Drawing on the driver’s driving experience and referring to the principle of radar scanning and the principle of image recognition, a virtual radar model was constructed to generate a virtual radar map. The virtual radar map was used to describe the position relationship between the traction spraying robot and the planned path. Combined with the deep reinforcement learning method, all possible robot driving actions under the current virtual radar map were scored, and the best driving action was selected as the output of the network. In this study, a path-tracking algorithm was self-developed with a deep Q-network trained by driving the traction spraying robot in a simulated virtual environment. The algorithm was tested in both simulations and in a field to follow a typical ‘U’-shaped path. The simulation results showed that the proposed algorithm was able to achieve accurate path-tracking control of the spraying trailer. The field tests showed that when the vehicle speed was 0.36 m/s and 0.75 m/s, the maximum lateral deviation of the algorithm was 0.233 m and 0.266 m, the average lateral deviation was 0.071 m and 0.076 m, and the standard deviation was 0.051 m and 0.057 m, respectively. Compared with the algorithm based on the virtual radar model, the maximum lateral deviation was reduced by 56.37% and 51.54%, the average lateral deviation was reduced by 7.8% and 5.0%, and the standard deviation was reduced by 20.31% and 8.1%, respectively. The results showed that the Double-DQN-based navigation path-tracking control algorithm for the traction spraying robot in the orchard had higher path-tracking accuracy and driving stability, which could meet the actual operational requirements of traditional orchards. Full article

Automatic navigation (AN) is an essential component to ensure the safety of pesticide application in orchards, whereas precision variable-rate spraying (PVS) serves as an indispensable technology for reducing the application of pesticides and protecting the environment. At present, AN and PVS are not closely combined. In this case, a single three-dimension (3D) light detection and ranging (LIDAR) sensor is hereby adopted to sense the information of fruit trees around the robot and determine the region of interest (ROI). Moreover, two-dimensional (2D) processing is conducted over the point clouds within the ROI to obtain the center-of-mass coordinates of fruit trees, and determine the vertical distance of the robot to the center line of the fruit tree row (FTR) based on the FTR on both sides using the Random Sample Consensus (RANSAC) algorithm. Then, the robot is controlled to drive along the center line of the FTR. At the same time, the speed and position of the robot are determined by the encoder and inertial measurement unit (IMU), and the IMU corrects the information collected from the zoned canopy of the fruit trees. The results present a lateral deviation (LD) of less than 22 cm and a course deviation (CD) of less than 4.02° during AN. Compared with the traditional spraying (TS), the PVS applies 32.46%, 44.34% and 58.14% less pesticide application, air drift and ground loss, respectively. With the spraying effect guaranteed, the single 3D LIDAR, the encoder and IMU realize the AN and PVS of the robot, reduce the volume of pesticide application, ground loss and air drift, and effectively control the pollution caused by pesticides to the environment. Full article

In Southeast Asia, many varieties of citrus are grown in hilly areas. Compared with plain orchards, it is difficult for large spraying equipment to move in hilly orchards. Small spraying equipment can enter hilly orchards, but their spraying power cannot make droplets penetrate into the canopy, resulting in low deposition rates within the canopy. As a kind of unstructured narrow space, the branches within the canopy are interlaced, thus a flexible manipulator that can move within the canopy is required. In this paper, a novel remote-controlled, cable-driven target spray robot (CDTSR) was designed to achieve a precise spray within the canopy. It consisted of a small tracked vehicle, a cable-driven flexible manipulator (CDFM), and a spray system. The CDFM had six degrees of freedom driven by a cable tendon. The forward and inverse kinematics model of the CDFM were established and then the semispherical workspace was calculated. Furthermore, while considering precise control requirements, the dynamics equations were derived. The experimental results demonstrated that the CFDM could move dexterously within the canopy with interlacing branches to reach pests and diseases areas in the canopy. The entire operation took 3.5 s. This study solved the problem of a low spray deposition rate within a canopy and has potential applications in agricultural plant protection. Full article