Agricultural Robot Research Articles

基于势场蚁群算法的农业仓库物流搬运机器人路径优化研究

In the layout of modern agricultural warehouse logistics handling industry, it is an inevitable way to realize industrial upgrading by replacing people with mobile robots. Aiming at the problems that the existing obstacle avoidance control algorithm of agricultural handling robot is easy to fall into local optimal solution, and the operation process of agricultural warehouse logistics handling robot is prone to collision, the obstacle avoidance control of agricultural warehouse logistics handling robot is studied, and a control algorithm based on improved potential field ant colony is proposed. The moving trajectory of the agricultural warehouse logistics handling robot during the handling process is studied, and the spatial kinematics equation of the robot is given. The ant colony algorithm is used to optimize the classical artificial potential field algorithm to improve the global optimization ability and balance the interaction between gravity and repulsion. In the aspect of local area obstacle avoidance of agricultural storage and handling robots, the artificial potential field is optimized twice based on the strategy gradient algorithm. By analyzing the probability of the next action command, the randomness of the travel path selection when multiple robots work at the same time is improved. After testing, the path of the proposed control algorithm is the shortest, and under the condition of complex path planning, the number of collisions between robots is also significantly less than that of the traditional obstacle avoidance control algorithm. The practical application can meet the needs of improving the efficiency of warehouse logistics management.

基于扫描上下文和 NDT-ICP 融合方案的果园同步定位与绘图方法研究

Simultaneous localization and mapping (SLAM) is one of the key technologies for agricultural robots to build maps and localize in complex orchard environments and realize unmanned autonomous operations. Due to the complexity of the orchard environment, the single canopy feature and the diffuse reflection of light caused by the leaves, etc., the map construction process of the orchard environment leads to mismatch and increases the cumulative error of the map construction. Aiming at the above problems, this paper propose a navigation map construction method for orchard environment based on the fusion of Scan Context and NDT-ICP. The method firstly searches the Ring key quickly to get the candidate frames, and scores the similarity between the candidate frames and the current frame, and effectively detects the loopbacks by two-stage searching algorithm to reduce the false matches in the map of orchard environment. Meanwhile, a point cloud alignment method based on the fusion of normal distribution transform coarse alignment and iterative nearest point exact alignment is used to reduce the cumulative error of the orchard environment map. The results show that the improved algorithm compensates the drift of the point cloud map with higher mapping accuracy, better real-time performance, lower resource utilization, higher overlap between the trajectory estimation and the real trajectory, smoother loops, and a 4% reduction in CPU occupancy. In the complex orchard environment, the root mean square error and standard deviation of the trajectories of this paper's algorithm are 0.57 m and 0.19 m, which are 68% and 83% higher than those of the loop detection algorithms in the Lightweight Ground Optimized Lidar Trajectory Measurement and Multivariate Terrain Mapping (LeGO-LOAM), respectively. Accurate map construction and low drift pose estimation can be performed.The research algorithm effectively reduces the influence of mis-matching and large cumulative error in the process of map construction in the orchard environment, meets the demand for high-precision environmental mapping in the orchard environment, and provides technical support for promoting unmanned operation in the orchard environment.

SN-CNN: A Lightweight and Accurate Line Extraction Algorithm for Seedling Navigation in Ridge-Planted Vegetables

In precision agriculture, after vegetable transplanters plant the seedlings, field management during the seedling stage is necessary to optimize the vegetable yield. Accurately identifying and extracting the centerlines of crop rows during the seedling stage is crucial for achieving the autonomous navigation of robots. However, the transplanted ridges often experience missing seedling rows. Additionally, due to the limited computational resources of field agricultural robots, a more lightweight navigation line fitting algorithm is required. To address these issues, this study focuses on mid-to-high ridges planted with double-row vegetables and develops a seedling band-based navigation line extraction model, a Seedling Navigation Convolutional Neural Network (SN-CNN). Firstly, we proposed the C2f_UIB module, which effectively reduces redundant computations by integrating Network Architecture Search (NAS) technologies, thus improving the model’s efficiency. Additionally, the model incorporates the Simplified Attention Mechanism (SimAM) in the neck section, enhancing the focus on hard-to-recognize samples. The experimental results demonstrate that the proposed SN-CNN model outperforms YOLOv5s, YOLOv7-tiny, YOLOv8n, and YOLOv8s in terms of the model parameters and accuracy. The SN-CNN model has a parameter count of only 2.37 M and achieves an mAP@0.5 of 94.6%. Compared to the baseline model, the parameter count is reduced by 28.4%, and the accuracy is improved by 2%. Finally, for practical deployment, the SN-CNN algorithm was implemented on the NVIDIA Jetson AGX Xavier, an embedded computing platform, to evaluate its real-time performance in navigation line fitting. We compared two fitting methods: Random Sample Consensus (RANSAC) and least squares (LS), using 100 images (50 test images and 50 field-collected images) to assess the accuracy and processing speed. The RANSAC method achieved a root mean square error (RMSE) of 5.7 pixels and a processing time of 25 milliseconds per image, demonstrating a superior fitting accuracy, while meeting the real-time requirements for navigation line detection. This performance highlights the potential of the SN-CNN model as an effective solution for autonomous navigation in field cross-ridge walking robots.

Image Segmentation-Based Oilseed Rape Row Detection for Infield Navigation of Agri-Robot

The segmentation and extraction of oilseed rape crop rows are crucial steps in visual navigation line extraction. Agricultural autonomous navigation robots face challenges in path recognition in field environments due to factors such as complex crop backgrounds and varying light intensities, resulting in poor segmentation and slow detection of navigation lines in oilseed rape crops. Therefore, this paper proposes VC-UNet, a lightweight semantic segmentation model that enhances the U-Net model. Specifically, VGG16 replaces the original backbone feature extraction network of U-Net, Convolutional Block Attention Module (CBAM) are integrated at the upsampling stage to enhance focus on segmentation targets. Furthermore, channel pruning of network convolution layers is employed to optimize and accelerate the model. The crop row trapezoidal ROI regions are delineated using end-to-end vertical projection methods with serialized region thresholds. Then, the centerline of oilseed rape crop rows is fitted using the least squares method. Experimental results demonstrate an average accuracy of 94.11% for the model and an image processing speed of 24.47 fps/s. After transfer learning for soybean and maize crop rows, the average accuracy reaches 91.57%, indicating strong model robustness. The average yaw angle deviation of navigation line extraction is 3.76°, with a pixel average offset of 6.13 pixels. Single image transmission time is 0.009 s, ensuring real-time detection of navigation lines. This study provides upper-level technical support for the deployment of agricultural robots in field trials.

Research on Corn Leaf and Stalk Recognition and Ranging Technology Based on LiDAR and Camera Fusion.

Corn, as one of the three major grain crops in China, plays a crucial role in ensuring national food security through its yield and quality. With the advancement of agricultural intelligence, agricultural robot technology has gained significant attention. High-precision navigation is the basis for realizing various operations of agricultural robots in corn fields and is closely related to the quality of operations. Corn leaf and stalk recognition and ranging are the prerequisites for achieving high-precision navigation and have attracted much attention. This paper proposes a corn leaf and stalk recognition and ranging algorithm based on multi-sensor fusion. First, YOLOv8 is used to identify corn leaves and stalks. Considering the large differences in leaf morphology and the large changes in field illumination that lead to discontinuous identification, an equidistant expansion polygon algorithm is proposed to post-process the leaves, thereby increasing the average recognition completeness of the leaves to 86.4%. Secondly, after eliminating redundant point clouds, the IMU data are used to calculate the confidence of the LiDAR and depth camera ranging point clouds, and point cloud fusion is performed based on this to achieve high-precision ranging of corn leaves. The average ranging error is 2.9 cm, which is lower than the measurement error of a single sensor. Finally, the stalk point cloud is processed and clustered using the FILL-DBSCAN algorithm to identify and measure the distance of the same corn stalk. The algorithm combines recognition accuracy and ranging accuracy to meet the needs of robot navigation or phenotypic measurement in corn fields, ensuring the stable and efficient operation of the robot in the corn field.

Advancing Robotics and Control Systems in West Africa: Opportunities, Challenges, and Future Directions

Robotics and control systems are poised to significantly impact West Africa's socio-economic landscape, offering transformative potential across various sectors. This review examines the current state, challenges, and future directions of robotics and control systems in the region. Robotics technology, while still in its nascent stage in West Africa, presents opportunities for substantial advancements in agriculture, industry, and healthcare. Key areas of focus include the fundamental algebraic methods used in robotics, such as kinematics, dynamic modeling, and control theory, which underpin the design and optimization of robotic systems. The review highlights the potential applications of robotics in agriculture, where automation can enhance productivity and efficiency, and in healthcare, where surgical robots and rehabilitation devices offer improved patient outcomes. However, significant barriers such as limited access to advanced technology, high costs, and inadequate infrastructure pose challenges to widespread adoption. Training and capacity building are critical for fostering a skilled workforce and advancing the robotics industry. Investment in education, specialized training, and international collaborations can address current limitations and support the development of a robust robotics ecosystem. Looking ahead, emerging technologies like artificial intelligence, collaborative robots, and advanced sensing are expected to drive future innovations. Strategic initiatives, including government policies, infrastructure development, and innovation hubs, are essential to realizing the full potential of robotics in West Africa. By overcoming existing challenges and leveraging opportunities for growth, the region can harness robotics and control systems to drive economic development and improve quality of life. Keywords: Robotics, Control Systems, West Africa, Opportunities, Challenges, Future Directions.

End-to-end stereo matching network with two-stage partition filtering for full-resolution depth estimation and precise localization of kiwifruit for robotic harvesting

Full-resolution depth estimation within operational space of robotic arms and accurate localization of kiwifruits is very important for automated harvesting. Depth estimation is expected to be accurate and full-resolution while current depth estimation methods are susceptible to depth missing due to occlusion and uneven illumination. And depth estimation mostly focuses on fruit localization, while obstacles such as branches and wires, which can affect harvesting strategy, have not been considered. This paper localized kiwifruits based on bounding boxes output by YOLOv8m and full-resolution depth from an end-to-end stereo matching network, i.e., LaC-Gwc Net, which was trained after generating a stereo matching dataset by proposing a two-stage partition filtering algorithm. Results showed that LaC-Gwc Net achieved an end-point error (EPE) of 3.8 pixels, which means that accurate depth estimation can also be achieved for thin obstacles such as the branches and the wires. Additionally, YOLOv8m obtained acceptable results in detecting kiwifruits and their calyxes, reaching mean average precision (mAP) of 93.1% and detection speed of 7.0 ms. The methodology obtained only kiwifruit localization error of 4.0 mm on the Z-axis, which meets requirements of robotic harvesting. Furthermore, this study considered the localization of obstacles in kiwifruit orchards, providing high-precision full-resolution depth estimation for agricultural harvesting robots.

Enhancing Agriculture through AloT and Data Analytics Middleware

Middleware in Agriculture delves into the data analytics role of systematic Artificial Intelligence of Things (AIoT) in convergence agriculture with smart farming. AIoT devices, remote diagnostic data analytics platforms, data analytics, formal recognition, and vision learning have generated both the amount and nature of work in rural areas. The reason for the developing changes in the global population by age is the bias in the distribution of food resources, as well as changes in climate change and the soil condition of compost. Data scientists are soon attempting to dive convergence Internet of Things (IoT) advances in savvy cultivating to support ranchers in producing better seeds, crop assurance, and manures utilizing AIoT convergence technology[1]. This distinguishes consumer-provider-administrator as a service subscriber roleon the platform, which goes for earning the nation's economy and the profitability of ranchers. The central regions where AIoT begins to emerge are agricultural robots, scrutiny, and soil and yield observation. While middleware technology for data analysis is applied, there is a great advantage of analyzing regional observation data at a real-time level. Based on this analysis method, it will spread to high-tech industries connected to production, processing, and consumption, which are subdivided areas of each precision agriculture. In this sense, ranchers are utilizing sensors for data analysis and soil information in a detailed way to gather a model that the executives dynamic platforms might use for further examination. Through an outline of real-time data analytics AIoT applications in the farming convergence industry, this paper tries a trust data basement forecast to the farming protocols[2]. It starts with a roadmap to the AIoT and an investigation of real-time data strategies utilized in the horticultural industry data analytics using Universal Middleware, service platforms, vision learning, and dynamic statistics. This study advances a dynamic platform examination of the literature on data resources and how AIoT is utilized in farming convergence. This study looked at the unstructured systems of modern agriculture, divided these processes into devices and services, and presented a systematic formalization model. It should contribute to the integration of segmented data connectivity in the AIoT field. The enhanced research model contributes to the discovery of numerous untapped additional services between devices and services.

Optimizing Orchard Planting Efficiency with a GIS-Integrated Autonomous Soil-Drilling Robot

A typical orchard’s mechanical operation consists of three or four stages: lining and digging for plantation, moving the seedling from nurseries to the farm, moving the seedling to the planting hole, and planting the seedling in the hole. However, the digging of the planting hole is the most time-consuming operation. In fruit orchards, the use of robots is increasingly becoming more prevalent to increase operational efficiency. They offer practical and effective services to both industry and people, whether they are assigned to plant trees, reduce the use of chemical fertilizers, or carry heavy loads to relieve staff. Robots can operate for extended periods of time and can be highly adept at repetitive tasks like planting many trees. The present study aims to identify the locations for planting trees in orchards using geographic information systems (GISs), to develop an autonomous drilling machine and use the developed robot to open planting holes. There is no comparable study on autonomous hole planting in the literature in this regard. The agricultural mobile robot is a four=wheeled nonholonomic robot with differential steering and forwarding capability to stable target positions. The designed mobile robot can be used in fully autonomous, partially autonomous, or fully manual modes. The drilling system, which is a y-axis shifter driven by a DC motor with a reducer includes an auger with a 2.1 HP gasoline engine. SOLIDWORKS 2020 software was used for designing and drawing the mobile robot and drilling system. The Microsoft Visual Basic.NET programming language was used to create the robot navigation system and drilling mechanism software. The cross-track error (XTE), which determines the distances between the actual and desired holes positions, was utilized to analyze the steering accuracy of the mobile robot to the drilling spots. Consequently, the average of the arithmetic means was determined to be 4.35 cm, and the standard deviation was 1.73 cm. This figure indicates that the suggested system is effective for drilling plant holes in orchards.

Study on Chassis Leveling Control of a Three-Wheeled Agricultural Robot

Three-wheeled agricultural robots possess the advantages of high flexibility, strong maneuverability, and low cost. They can adapt to various complex terrains and operational environments, making them highly valuable in the fields of crop planting, harvesting, irrigation, and more. However, the horizontal stability of the three-wheeled agricultural robot chassis is compromised when working in harsh terrain, significantly impacting the overall operational quality and safety. To address this issue, this study designed a leveling system based on active suspension and proposed a stepwise leveling method based on an adaptive dual-loop composite control strategy (ADLCCS-SLM). Firstly, in the overall control of the three-wheeled chassis, a stepwise leveling method (SLM) was introduced. This method allows for rapid leveling by incrementally adjusting one or two suspensions, effectively avoiding the complex interactions between suspension components encountered in traditional methods involving the simultaneous linkage of three suspensions. Next, in terms of suspension actuator control, an adaptive dual-loop composite control strategy (ADLCCS) was proposed. This strategy employs a dual-loop composite control both internally and externally and utilizes an improved adaptive genetic algorithm to adjust critical control parameters. This adaptation optimizes the chassis leveling performance across various road conditions. Finally, the effectiveness of the proposed ADLCCS-SLM was validated through simulation and experimental testing. The test results showed that the control effect of the proposed method was significant. Compared to the traditional multi-suspension linkage leveling method based on PID, the peak values of pitch angle and roll angle were reduced by 31.8% and 33.3%, respectively.

Multi-area collision-free path planning and efficient task scheduling optimization for autonomous agricultural robots

Collision-free path planning and task scheduling optimization in multi-region operations of autonomous agricultural robots present a complex coupled problem. In addition to considering task access sequences and collision-free path planning, multiple factors such as task priorities, terrain complexity of farmland, and robot energy consumption must be comprehensively addressed. This study aims to explore a hierarchical decoupling approach to tackle the challenges of multi-region path planning. Firstly, we conduct path planning based on the A* algorithm to traverse paths for all tasks and obtain multi-region connected paths. Throughout this process, factors such as path length, turning points, and corner angles are thoroughly considered, and a cost matrix is constructed for subsequent optimization processes. Secondly, we reformulate the multi-region path planning problem into a discrete optimization problem and employ genetic algorithms to optimize the task sequence, thus identifying the optimal task execution order under energy constraints. We finally validate the feasibility of the multi-task planning algorithm proposed by conducting experiments in an open environment, a narrow environment and a large-scale environment. Experimental results demonstrate the method's capability to find feasible collision-free and cost-optimal task access paths in diverse and complex multi-region planning scenarios.

Enhancing the Structural Integrity and Performance of an Agricultural Robot with Caterpillar Tracks: A Comprehensive Deformation Analysis

Enhancing the Structural Integrity and Performance of an Agricultural Robot with Caterpillar Tracks: A Comprehensive Deformation Analysis

Pineapple Detection with YOLOv7-Tiny Network Model Improved via Pruning and a Lightweight Backbone Sub-Network

High-complexity network models are challenging to execute on agricultural robots with limited computing capabilities in a large-scale pineapple planting environment in real time. Traditional module replacement often struggles to reduce model complexity while maintaining stable network accuracy effectively. This paper investigates a pineapple detection framework with a YOLOv7-tiny model improved via pruning and a lightweight backbone sub-network (the RGDP-YOLOv7-tiny model). The ReXNet network is designed to significantly reduce the number of parameters in the YOLOv7-tiny backbone network layer during the group-level pruning process. Meanwhile, to enhance the efficacy of the lightweight network, a GSConv network has been developed and integrated into the neck network, to further diminish the number of parameters. In addition, the detection network incorporates a decoupled head network aimed at separating the tasks of classification and localization, which can enhance the model’s convergence speed. The experimental results indicate that the network before pruning optimization achieved an improvement of 3.0% and 2.2%, in terms of mean average precision and F1 score, respectively. After pruning optimization, the RGDP-YOLOv7-tiny network was compressed to just 2.27 M in parameter count, 4.5 × 109 in computational complexity, and 5.0MB in model size, which were 37.8%, 34.1%, and 40.7% of the original YOLOv7-tiny network, respectively. Concurrently, the mean average precision and F1 score reached 87.9% and 87.4%, respectively, with increases of 0.8% and 1.3%. Ultimately, the model’s generalization performance was validated through heatmap visualization experiments. Overall, the proposed pineapple object detection framework can effectively enhance detection accuracy. In a large-scale fruit cultivation environment, especially under the constraints of hardware limitations and limited computational power in the real-time detection processes of agricultural robots, it facilitates the practical application of artificial intelligence algorithms in agricultural engineering.

Internet of robotic things with a local LoRa network for teleoperation of an agricultural mobile robot using a digital shadow

In unstructured agricultural fields where autonomous navigation is challenging and demands additional safety, the operator’s experience and knowledge are essential for supervising operations and making decisions beyond the robot’s autonomous capabilities. Local networks with long-range wireless communication combined with digital twin concepts are promising solutions that can be used for robot teleoperation. The purpose of this study was to demonstrate the feasibility of supervising a mobile robot inside berry orchards using a digital shadow from a long-range distance (between 300 and 3000 m), with the primary objective of assisting the robot in navigating in complex situations such as row-end turning. This involved creating a virtual representation of the robot that mirrors its state and actions, allowing the remote operator to monitor and guide the robot effectively. The system comprised a GPS-based navigation controller with collision avoidance sensors, two sets of LoRa transmitters and repeaters, a simulation environment with a digital shadow of the robot, and a graphical user interface for the remote operator. Information about the digital shadow’s state, including location, orientation, and distances to obstacles, was received as a message by the LoRa gateway and was used to update the path for the actual robot that interfaced with the Robot Operating System (ROS). The main research hypothesis aimed to test the quality of the LoRa communication link between the robot and the operator, as well as the robustness of the robot’s control system, with an emphasis on the architecture, communication link, and situation awareness creation. Preliminary results showed that depending on the environment, the average packet loss was 12% at distances of approximately 2300 m. Our results highlight some of the core technical challenges that need to be addressed for an effective teleoperation system, including latency, stability, and the limited range of wireless communication. Future works involves evaluating the performance and reliability of the proposed method under different field conditions and scenarios, as well as considering the use of the 5G network for a significant improvement in data transmission speed, navigation efficiency, and visual feedback. Upon successful implementation, this study has the potential to enhance the efficiency and safety of robot navigation, providing a practical solution for remote supervision in challenging environments.

YOLOv7-Branch: A Jujube Leaf Branch Detection Model for Agricultural Robot.

The intelligent harvesting technology for jujube leaf branches presents a novel avenue for enhancing both the quantity and quality of jujube leaf tea, whereas the precise detection technology for jujube leaf branches emerges as a pivotal factor constraining its development. The precise identification and localization of jujube leaf branches using real-time object detection technology are crucial steps toward achieving intelligent harvesting. When integrated into real-world scenarios, issues such as the background noise introduced by tags, occlusions, and variations in jujube leaf morphology constrain the accuracy of detection and the precision of localization. To address these issues, we describe a jujube leaf branch object detection network based on YOLOv7. First, the Polarized Self-Attention module is embedded into the convolutional layer, and the Gather-Excite module is embedded into the concat layer to incorporate spatial information, thus achieving the suppression of irrelevant information such as background noise. Second, we incorporate implicit knowledge into the Efficient Decoupled Head and replace the original detection head, enhancing the network's capability to extract deep features. Third, to address the issue of imbalanced jujube leaf samples, we employ Focal-EIoU as the bounding box loss function to expedite the regression prediction and enhance the localization accuracy of the model's bounding boxes. Experiments show that the precision of our model is 85%, which is increased by 3.5% compared to that of YOLOv7-tiny. The mAP@0.5 value is 83.7%. Our model's recognition rate, recall and mean average precision are superior to those of other models. Our method could provide technical support for yield estimation in the intelligent management of jujube orchards.

An improved DWA algorithm in agricultural robot

An improved DWA algorithm in agricultural robot

Agricultural Tractor Test: A Bibliometric Review

Agricultural tractors are an essential agricultural power source. Therefore, the scientific literature tests have described agricultural tractors’ evolution over time and determined future trends. This paper uses bibliometric tools to assess the agricultural evolution of tractor testing from 1969 to 2022 to ascertain the publication’s scientific perspective on operational, ergonomic, and energy performance. We searched for relevant research in the Scopus and Web of Science (WOS) databases. The data were processed in RStudio software version 4.4.1, and we used elaborated bibliometric maps to research evolution, major journals, studies, countries, and keywords. The first research mainly concerned the development of new wheelsets, more efficient engines, and fuel consumption prediction models. After the 2000s, environmental protocols contributed to increasing publications on biofuels and renewable energies. Recently, an intense process of robotization in autonomous vehicles has improved to allow the replacement of combustion engines. Ergonomics and safety have been less recurrent topics in recent years, indicating a stable level in the actual research. New machine control models involving artificial intelligence are currently applied to obtain test results without using the machine in the field. These virtual models reduce costs and optimize resources. The most common terms were “tractor” and “agricultural machinery”. The terms “Electric tractor”, “agricultural robots”, and “Matlab” indicate solid trends for future research.

Positive public attitudes towards agricultural robots

Robot technologies could lead to radical changes in farming. But what does the public know and think about agricultural robots? Recent experience with other agricultural technologies—such as plant genetic engineering—shows that public perceptions can influence the pace and direction of innovation, so understanding perceptions and how they are formed is important. Here, we use representative data from an online survey (n = 2269) to analyze public attitudes towards crop farming robots in Germany—a country where new farming technologies are sometimes seen with skepticism. While less than half of the survey participants are aware of the use of robots in agriculture, general attitudes are mostly positive and the level of interest is high. A framing experiment suggests that the type of information provided influences attitudes. Information about possible environmental benefits increases positive perceptions more than information about possible food security and labor market effects. These insights can help design communication strategies to promote technology acceptance and sustainable innovation in agriculture.

Algorithm for Corn Crop Row Recognition during Different Growth Stages Based on ST-YOLOv8s Network

Corn crop row recognition during different growth stages is a major difficulty faced by the current development of visual navigation technology for agricultural robots. In order to solve this problem, an algorithm for recognizing corn crop rows during different growth stages is presented based on the ST-YOLOv8s network. Firstly, a dataset of corn crop rows during different growth stages, including the seedling stage and mid-growth stage, is constructed in this paper; secondly, an improved YOLOv8s network, in which the backbone network is replaced by the swin transformer (ST), is proposed in this paper for detecting corn crop row segments; after that, an improved supergreen method is introduced in this paper, and the segmentation of crop rows and background within the detection frame is achieved utilizing the enhanced method; finally, the corn crop row lines are identified using the proposed local–global detection method, which detects the local crop rows first, and then detects the global crop rows. The corn crop row segment detection experiments show that the mean average precision (MAP) of the ST-YOLOv8s network during different growth stages increases by 7.34%, 11.92%, and 4.03% on average compared to the MAP of YOLOv5s, YOLOv7, and YOLOv8s networks, respectively, indicating that the ST-YOLOv8s network has a better crop row segment detection effect compared to the comparison networks. Corn crop row line detection experiments show that the accuracy of the local–global detection method proposed in this paper is improved by 17.38%, 10.47%, and 5.99%, respectively, compared with the accuracy of the comparison method; the average angle error is reduced by 3.78°, 1.61°, and 0.7°, respectively, compared with the average angle error of the comparison method; and the average fitting time is reduced by 5.30 ms, 18 ms, and 33.77 ms, respectively, compared with the average fitting time of the comparison method, indicating that the local–global detection method has a better crop row line detection effect compared to the comparison method. In summary, the corn crop row recognition algorithm proposed in this paper can well accomplish the task of corn crop row recognition during different growth stages and contribute to the development of crop row detection technology.

AI Robots in Various Sector

The convergence of Artificial Intelligence (AI) and robotics is revolutionizing multiple sectors, enhancing productivity, safety, and quality. In manufacturing, AI-driven robots automate assembly lines, optimize logistics, and perform precision tasks. These robots adapt to changing production demands, improving efficiency and reducing errors. In agriculture, vision-based AI assists robots in weed detection, crop scouting, disease identification, and harvesting. By integrating AI algorithms with sensors, agricultural robots enhance yield prediction and resource management. In healthcare, surgical robots aid in minimally invasive procedures, while AI algorithms analyze medical images for early disease detection. The synergy between AI and robotics holds immense potential, reshaping industries and advancing human well-being