Search Results (65)

Crop row identification and navigation line extraction are essential components for enabling autonomous operations of agricultural machinery. Aiming at the soybean–maize strip intercropping system, this study proposes a LiDAR-based algorithm for crop row detection and navigation line extraction. The proposed method consists of four primary stages: point cloud preprocessing, crop row region identification, feature point clustering, and navigation line extraction. Specifically, a combination of K-means and Euclidean clustering algorithms is employed to extract feature points representing crop rows. The central lines of the crop rows are then fitted using the least squares method, and a stable navigation path is constructed based on angle bisector principles. Field experiments were conducted under three representative scenarios: broken rows with missing plants, low occlusion, and high occlusion. The results demonstrate that the proposed method exhibits strong adaptability and robustness across various environments, achieving over 80% accuracy in navigation line extraction, with up to 90% in low-occlusion settings. The average navigation angle was controlled within 0.28°, with the minimum reaching 0.17°, and the average processing time remained below 75.62 ms. Moreover, lateral deviation tests confirmed the method’s high precision and consistency in path tracking, validating its feasibility and practicality for application in strip intercropping systems. Full article

Autonomous navigation technology plays a crucial role in advancing smart agriculture by enhancing operational efficiency, optimizing resource utilization, and reducing labor dependency. With the rapid integration of information technology, modern agricultural machinery increasingly incorporates advanced techniques such as high-precision positioning, environmental perception, path planning, and path-tracking control. This paper presents a comprehensive review of recent advancements in these core technologies, systematically analyzing their methodologies, advantages, and application scenarios. Despite notable progress, considerable challenges persist, primarily due to the unstructured nature of farmland, varying terrain conditions, and the demand for robust and adaptive control strategies. This review also discusses current limitations and outlines prospective research directions, aiming to provide valuable insights for the future development and practical deployment of autonomous navigation systems in agricultural machinery. Future research is expected to focus on enhancing multi-modal perception under occlusion and variable lighting conditions, developing terrain-aware path planning algorithms that adapt to irregular field boundaries and elevation changes and designing robust control strategies that integrate model-based and learning-based approaches to manage disturbances and non-linearity. Furthermore, tighter integration among perception, planning, and control modules will be crucial for improving system-level intelligence and coordination in real-world agricultural environments. Full article

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

As a core component of agricultural machinery autonomous navigation, path tracking control holds significant research value. The pure pursuit algorithm has become a prevalent method for agricultural vehicle navigation due to its effectiveness at low speeds, yet its performance critically depends on the selection of the look-ahead distance. The conventional approaches require extensive parameter tuning due to the complex influencing factors, while fixed look-ahead distances struggle to balance the tracking accuracy and adaptability. Considerable effort is required to fine-tune the system to achieve optimal performance, which directly affects the accuracy of the path tracking and the results in the cumbersome task of selecting an appropriate goal point for the tracking path. To address these challenges, this paper introduces a pure pursuit algorithm for high-clearance sprayers in agricultural machinery, utilizing a self-adjusting look-ahead distance. By developing a kinematic model of the pure pursuit algorithm for agricultural machinery, an evaluation function is then employed to estimate the pose of the machinery and identify the corresponding optimal look-ahead distance within the designated area. This is done based on the principle of minimizing the overall error, enabling the dynamic and adaptive optimization of the look-ahead distance within the pure pursuit algorithm. Finally, this algorithm was verified in simulations and bumpy field tests under various different conditions, with the average value of the lateral error reduced by more than 0.06 m and the tuning steps also significantly reduced compared to the fixed look-ahead distance in field tests. The tracking accuracy has been improved and the applicability of the algorithm for rapid deployment has been enhanced. Full article

With the acceleration of agricultural intelligent transformation, deep reinforcement learning (DRL), leveraging its adaptive perception and decision-making capabilities in complex environments, has emerged as a pivotal technology in advancing the intelligent upgrade of agricultural machinery and equipment. For example, in UAV path optimization, DRL can help UAVs plan more efficient flight paths to cover more areas in less time. To enhance the systematicity and credibility of this review, this paper systematically examines the application status, key issues, and development trends of DRL in agricultural scenarios, based on the research literature from mainstream Chinese and English databases spanning from 2018 to 2024. From the perspective of algorithm–hardware synergy, the article provides an in-depth analysis of DRL’s specific applications in agricultural ground platform navigation, path planning for intelligent agricultural end-effectors, and autonomous operations of low-altitude unmanned aerial vehicles. It highlights the technical advantages of DRL by integrating typical experimental outcomes, such as improved path-tracking accuracy and optimized spraying coverage. Meanwhile, this paper identifies three major challenges facing DRL in agricultural contexts: the difficulty of dynamic path planning in unstructured environments, constraints imposed by edge computing resources on algorithmic real-time performance, and risks to policy reliability and safety under human–machine collaboration conditions. Looking forward, the DRL-driven smart transformation of agricultural machinery will focus on three key aspects: (1) The first aspect is developing a hybrid decision-making architecture based on model predictive control (MPC). This aims to enhance the strategic stability and decision-making interpretability of agricultural machinery (like unmanned tractors, harvesters, and drones) in complex and dynamic field environments. This is essential for ensuring the safe and reliable autonomous operation of machinery. (2) The second aspect is designing lightweight models that support edge-cloud collaborative deployment. This can meet the requirements of low-latency responses and low-power operation in edge computing scenarios during field operations, providing computational power for the real-time intelligent decision-making of machinery. (3) The third aspect is integrating meta-learning with self-supervised mechanisms. This helps improve the algorithm’s fast generalization ability across different crop types, climates, and geographical regions, ensuring the smart agricultural machinery system has broad adaptability and robustness and accelerating its application in various agricultural settings. This paper proposes research directions from three key dimensions-“algorithm capability enhancement, deployment architecture optimization, and generalization ability improvement”-offering theoretical references and practical pathways for the continuous evolution of intelligent agricultural equipment. Full article

A pure pursuit method based on an improved sparrow search algorithm is proposed to address low path-tracking accuracy of intelligent agricultural machinery in complex farmland environments. Firstly, we construct a function relating speed to look-ahead distance and develop a fitness function based on the prototype’s speed and pose deviation. Subsequently, an improved sparrow search algorithm (ISSA) is employed to adjust the pure pursuit model’s speed and look-ahead distance dynamically. Finally, improvements are made to the initialization of the original algorithm and the position update method between different populations. Simulation results indicate that the improved sparrow search algorithm exhibits faster convergence speed and better capability to escape local extrema. The real vehicle test results show that the proposed algorithm achieves an average lateral deviation of approximately 3 cm, an average heading deviation below 5°, an average stabilization distance under 5 m, and an average navigation time of around 46 s during path tracking. These results represent reductions of 51.25%, 30.62%, 49.41%, and 10.67%, respectively, compared to the traditional pure pursuit model. Compared to the pure pursuit model that only dynamically adjusts the look-ahead distance, the proposed algorithm shows reductions of 34.11%, 24.96%, 32.13%, and 11.23%, respectively. These metrics demonstrate significant improvements in path-tracking accuracy, pose correction speed, and path-tracking efficiency, indicating that the proposed algorithm can serve as a valuable reference for path-tracking research in complex agricultural environments. Full article

The terrain in hilly and mountainous areas is complex, and the level of agricultural mechanization is low. This article systematically reviews the research progress of key technologies for agricultural machinery power chassis in hilly and mountainous areas, and conducts an analysis of five aspects: the power system, walking system, steering system, leveling system, and automatic navigation and path tracking control system. In this manuscript, (1) in terms of the power system, the technical characteristics and application scenarios of mechanical, hydraulic, and electric drive systems were compared. (2) In terms of the walking system, the performance differences between wheeled, crawler, legged, and composite walking devices and the application of suspension systems in agricultural machinery chassis were discussed. (3) In terms of the steering system, the steering characteristics of wheeled chassis and crawler chassis were analyzed, respectively. (4) In terms of the leveling system, the research progress on hydraulic and electric leveling mechanisms, as well as intelligent leveling control algorithms, was summarized. (5) The technology of automatic navigation and path tracking for agricultural machinery chassis was discussed, focusing on multi-sensor fusion and advanced control algorithms. In the future, agricultural machinery chassis will develop towards the directions of intelligence, automation, greening, being lightweight, and being multi-functionality. Full article

Single-track electric agricultural chassis plays a vital role in autonomous navigation and driving operations in hilly and mountainous regions, where its path tracking performance directly affects the operational accuracy and stability. However, in complex farmland environments, traditional methods often suffer from frequent turning and large tracking errors due to variable path curvature, uneven terrain, and track slippage. To address these issues, this paper proposes a path tracking algorithm combining a segmented preview model with variable universe fuzzy control, enabling dynamic adjustment of the preview distance for better curvature adaptation. Additionally, a heading deviation prediction model based on Support Vector Regression (SVR) optimized by Particle Swarm Optimization (PSO) is introduced, and a steering angle compensation controller is designed to improve the turning accuracy. Simulation and field experiments show that, compared with fixed preview distance and fixed-universe fuzzy control methods, the proposed algorithm reduces the average number of turns per control cycle by 30.19% and 18.23% and decreases the average lateral error by 34.29% and 46.96%, respectively. These results confirm that the proposed method significantly enhances path tracking stability and accuracy in complex terrains, providing an effective solution for autonomous navigation of agricultural machinery. Full article

Precision positioning in orchards relies on Global Navigation Satellite System and Inertial Navigation System (GNSS/INS) integration. However, dense foliage often causes GNSS blockages, degrading accuracy and robustness. This paper proposes an optimized GNSS/INS integrated navigation method based on a hybrid Gated Recurrent Unit (GRU)–Transformer model (GRU-T). The GRU–Transformer hybrid dynamically adjusts the process noise covariance matrix within an error-state Extended Kalman Filter (ES-EKF) framework to address non-stationary noise and signal outages. Forest field tests demonstrate that GRU-T significantly improves positioning accuracy. Compared with the conventional ES-EKF, the proposed method achieves reductions in position root mean square error (PRMSE) of 48.74% (East), 41.94% (North), and 61.59% (Up), and reductions in velocity root mean square error (VRMSE) of 71.5% (East), 39.31% (North), and 56.48% (Up) in the East–North–Up (ENU) coordinate frame. The GRU-T model effectively captures both short- and long-term temporal dependencies and meets real-time, high-frequency sampling requirements. These results indicate that the GRU–Transformer hybrid model enhances the accuracy and robustness of GNSS/INS navigation in complex orchard environments, offering technical support for high-precision positioning in intelligent agricultural machinery systems. Full article

This study is situated against the background of shortages in the agricultural labor force and shortages of cultivated land. In order to improve the intelligence level and operational efficiency of agricultural machinery and solve the problems of difficulties in recognizing navigation lines and a lack of real-time performance of transplanters in the crop-free ridge environment, we propose a crop-free-ridge navigation line recognition method based on an improved YOLOv8 segmentation algorithm. First, this method reduces the parameters and computational complexity of the model by replacing the YOLOv8 backbone network with MobileNetV4 and the feature extraction module C2f with ShuffleNetV2, thereby improving the real-time segmentation of crop-free ridges. Second, we use the least-squares method to fit the obtained point set to accurately obtain navigation lines. Finally, the method is applied to testing and analyzing the field experimental ridges. The results showed that the average precision of the improved neural network model using this method was 90.4%, with a Params of 1.8 M, a FLOPs of 8.8 G, and an FPS of 49.5. The results indicate that the model maintains high accuracy while significantly outperforming Mask-RCNN, YOLACT++, YOLOv8, and YOLO11 in terms of computational speed. The detection frame rate increased significantly, improving the real-time performance of detection. This method uses the least-squares method to fit the 55% ridge contour feature points under the picture, and the fitting navigation line shows no large deviation compared with the image ridge centerline; the result is better than that of the RANSAC fitting method. The research results indicate that this method significantly reduces the size of the model parameters and improves the recognition speed, providing a more efficient solution for the autonomous navigation of intelligent carrier aircraft. Full article

Accurate crop row detection is an important foundation for agricultural machinery to realize autonomous operation. Existing methods often compromise between real-time performance and detection accuracy, limiting their practical field applicability. This study develops a high-precision, efficient crop row detection algorithm specifically optimized for soybean–corn compound planting conditions, addressing both computational efficiency and recognition accuracy. In this paper, a real-time soybean–corn crop row detection method based on GD-YOLOv10n-seg with principal component analysis (PCA) fitting was proposed. Firstly, the dataset of soybean–corn seedling crop rows was established, and the images were labeled with line labels. Then, an improved model GD-YOLOv10n-seg model was constructed by integrating GhostModule and DynamicConv into the YOLOv10n-segmentation model. The experimental results showed that the improved model performed better in MPA and MIoU, and the model size was reduced by 18.3%. The crop row center lines of the segmentation results were fitted by PCA, where the fitting accuracy reached 95.08%, the angle deviation was 1.75°, and the overall processing speed was 61.47 FPS. This study can provide an efficient and reliable solution for agricultural autonomous navigation operations such as weeding and pesticide application under a soybean–corn compound planting mode. Full article

Based on the characteristics of small agricultural machinery in terms of flexibility and high efficiency when operating in small plots of hilly and mountainous areas, as well as the demand for improving the automation and intelligence levels of agricultural machinery, this paper conducted research on the path tracking control of the automatic navigation operation of a crawler sprayer. Based on the principles of the kinematic model and the position prediction model of the agricultural machinery chassis, a pure pursuit controller based on adaptive look-ahead distance was designed for the tracked motion chassis. Using a lightweight crawler sprayer as the research platform, integrating onboard industrial control computers, sensors, communication modules, and other hardware, an automatic navigation operation system was constructed, achieving precise control of the crawler sprayer during the path tracking process. Simulation test results show that the path tracking control method based on adaptive look-ahead distance has the characteristics of smooth control and small steady-state error. Field tests indicate that the crawler sprayer exhibits small deviations during path tracking, with an average absolute error of 2.15 cm and a maximum deviation of 4.08 cm when operating at a speed of 0.7 m/s. In the line-following test, with initial position deviations of 0.5 m, 1.0 m, and 1.5 m, the line-following times were 7.45 s, 11.91 s, and 13.66 s, respectively, and the line-following distances were 5.21 m, 8.34 m, and 9.56 m, respectively. The maximum overshoot values were 6.4%, 10.5%, and 12.6%, respectively. The autonomous navigation experiments showed a maximum deviation of 5.78 cm and a mean absolute error of 2.69 cm. The proportion of path deviations within ±5 cm and ±10 cm was 97.32% and 100%, respectively, confirming the feasibility of the proposed path tracking control method. This significantly enhanced the path tracking performance of the crawler sprayer while meeting the requirements for autonomous plant protection spraying operations. Full article

Farmland boundaries distinguish agricultural areas from non-agricultural areas, providing limits for field operations and navigation paths of agricultural machinery. However, in hilly regions, the irregularity of paddy field boundaries complicates the extraction of boundary information, hindering the widespread use of GNSS-based navigation systems in agricultural machinery. This paper focuses on the paddy field border prior to rice planting and utilizes machine vision and GNSS fusion technology to extract navigation line coordinates. First, the BiSeNet semantic segmentation network was employed to extract paddy field ridges. Second, the camera’s 3D attitude was obtained in real time using an Attitude and Heading Reference System (AHRS). A method and device based on the hydraulic profiling system were proposed to measure the camera’s height relative to the paddy field, providing a dynamic external reference. An improved inverse perspective transformation was applied to generate a bird’s-eye view of the paddy field ridges. Finally, a homogeneous coordinate transformation method was used to extract the navigation line coordinates, with the model and algorithms deployed on the Jetson AGX Xavier platform Field tests demonstrated a real-time segmentation speed of 26.31 fps, pixel segmentation accuracy of 92.43%, and an average intersection ratio of 90.62%. The average distance error of the extracted navigation line was 0.071 m, with a standard deviation of 0.039 m. The coordinate extraction took approximately 100 ms, meeting the accuracy and real-time requirements for navigation line extraction at the rice transplanter’s speed of 0.7 m s⁻¹, providing path information for subsequent autonomous navigation. Full article

Smart agricultural machinery is built upon traditional agricultural equipment, further integrating modern information technologies to achieve automation, precision, and intelligence in agricultural production. Currently, significant progress has been made in the autonomous operation and monitoring technologies of smart agricultural machinery in China. However, challenges remain, including poor adaptability to complex environments, high equipment costs, and issues with system implementation and standardization integration. To help industry professionals quickly understand the current state and promote the rapid development of smart agricultural machinery, this paper provides an overview of the key technologies related to autonomous operation and monitoring in China’s smart agricultural equipment. These technologies include environmental perception, positioning and navigation, autonomous operation and path planning, agricultural machinery status monitoring and fault diagnosis, and field operation monitoring. Each of these key technologies is discussed in depth with examples and analyses. On this basis, the paper analyzes the main challenges faced by the development of autonomous operation and monitoring technologies in China’s smart agricultural machinery sector. Furthermore, it explores the future directions for the development of autonomous operation and monitoring technologies in smart agricultural machinery. This research is of great importance for promoting the transition of China’s agricultural production towards automation and intelligence, improving agricultural production efficiency, and reducing reliance on human labor. Full article

Accurate rice row detection is critical for autonomous agricultural machinery navigation in complex paddy environments. Existing methods struggle with terrain unevenness, water reflections, and weed interference. This study aimed to develop a robust rice row detection method by integrating multi-sensor data and leveraging robot travelling prior information. A 3D point cloud acquisition system combining 2D LiDAR, AHRS, and RTK-GNSS was designed. A variable-threshold segmentation method, dynamically adjusted based on real-time posture perception, was proposed to handle terrain variations. Additionally, a clustering algorithm incorporating rice row spacing and robot path constraints was developed to filter noise and classify seedlings. Experiments in dryland with simulated seedlings and real paddy fields demonstrated high accuracy: maximum absolute errors of 59.41 mm (dryland) and 69.36 mm (paddy), with standard deviations of 14.79 mm and 19.18 mm, respectively. The method achieved a 0.6489° mean angular error, outperforming existing algorithms. The fusion of posture-aware thresholding and path-based clustering effectively addresses the challenges in complex rice fields. This work enhances the automation of field management, offering a reliable solution for precision agriculture in unstructured environments. Its technical framework can be adapted to other row crop systems, promoting sustainable mechanization in global rice production. Full article