The application of autonomous navigation in intelligent agriculture is becoming more and more extensive. Traditional navigation schemes in greenhouses, orchards, and other agricultural environments often have problems such as the inability to deal with an uneven illumination distribution, complex layout, highly repetitive and similar structures, and difficulty in receiving GNSS (Global Navigation Satellite System) signals. In order to solve this problem, this paper proposes a new tightly coupled LiDAR (Light Detection and Ranging) inertial odometry SLAM (LIO-SAM) framework named April-LIO-SAM. The framework innovatively uses Apriltag, a two-dimensional bar code widely used for precise positioning, pose estimation, and scene recognition of objects as a global positioning beacon to replace GNSS to provide absolute pose observation. The system uses three-dimensional LiDAR (VLP-16) and IMU (inertial measurement unit) to collect environmental data and uses Apriltag as absolute coordinates instead of GNSS to solve the problem of unreliable GNSS signal reception in greenhouses, orchards, and other agricultural environments. The SLAM trajectories and navigation performance were validated in a carefully built greenhouse and orchard environment. The experimental results show that the navigation map developed by the April-LIO-SAM yields a root mean square error of 0.057 m. The average positioning errors are 0.041 m, 0.049 m, 0.056 m, and 0.070 m, respectively, when the density of Apriltag is 3 m, 5 m, and 7 m. The navigation experimental results indicate that, at speeds of 0.4, 0.3, and 0.2 m/s, the average lateral deviation is less than 0.053 m, with a standard deviation below 0.034 m. The average heading deviation is less than 2.3°, with a standard deviation below 1.6°. The positioning stability experiments under interference conditions such as illumination and occlusion were carried out. It was verified that the system maintained a good stability under complex external conditions, and the positioning error fluctuation was within 3.0 mm. The results confirm that the robot positioning and navigation accuracy of mobile robots satisfy the continuity in the facility.

The vision-based recognition and localization system plays a crucial role in the unmanned harvesting of aquatic vegetables. After field investigation, factors such as illumination, shading, and computational cost have become the main difficulties restricting the identification and positioning of Brasenia schreberi. Therefore, this paper proposes a new lightweight detection method, YOLO-GS, which integrates feature information from both RGB and depth images for recognition and localization tasks. YOLO-GS employs the Ghost convolution module as a replacement for traditional convolution and innovatively introduces the C3-GS, a cross-stage module, to effectively reduce parameters and computational costs. With the redesigned detection head structure, its feature extraction capability in complex environments has been significantly enhanced. Moreover, the model utilizes Focal EIoU as the regression loss function to mitigate the adverse effects of low-quality samples on gradients. We have developed a data set of Brasenia schreberi that covers various complex scenarios, comprising a total of 1500 images. The YOLO-GS model, trained on this dataset, achieves an average accuracy of 95.7%. The model size is 7.95 MB, with 3.75 M parameters and a 9.5 GFLOPS computational cost. Compared to the original YOLOv5s model, YOLO-GS improves recognition accuracy by 2.8%, reduces the model size and parameter number by 43.6% and 46.5%, and offers a 39.9% reduction in computational requirements. Furthermore, the positioning errors of picking points are less than 5.01 mm in the X direction, 3.65 mm in the Y direction, and 1.79 mm in the Z direction. As a result, YOLO-GS not only excels with high recognition accuracy but also exhibits low computational demands, enabling precise target identification and localization in complex environments so as to meet the requirements of real-time harvesting tasks.

At present, there is a lack of effective and usable machinery in the harvesting of aquatic vegetables. The harvesting of most aquatic vegetables such as Brasenia schreberi relies entirely on manual labor, resulting in a high labor demand and labor shortages, which restricts the industrial development of aquatic vegetables. To address this problem, an intelligent harvesting system for the aquatic vegetable Brasenia schreberi was developed in response to the challenging working conditions associated with harvesting it. The system is composed of a catamaran mobile platform, a picking device, and a harvesting manipulator control system. The mobile platform, driven by two paddle wheels, is equipped with a protective device to prevent vegetable stem entanglement, making it suitable for shallow pond aquatic vegetable environments. The self-designed picking device rapidly harvests vegetables through lateral clamping and cutting. The harvesting manipulator control system incorporates harvesting posture perception based on the YOLO-GS recognition algorithm and combines it with an improved RRT algorithm for robotic arm path planning. The experimental results indicate that the intelligent harvesting system is suitable for aquatic vegetable harvesting and the improved RRT algorithm surpasses the traditional one in terms of the planning time and path length. The vision-based positioning error was 4.80 mm, meeting harvesting accuracy requirements. In actual harvest experiments, the system showed an average success rate of 90.0%, with an average picking time of 5.229 s per leaf, thus proving its feasibility and effectiveness.

As core equipment for precision agriculture, precision seeders directly impact the sustainability and economic efficiency of modern agriculture. With the increasing demands for precision farming and high-speed operation, precision seeders featuring state-of-the-art mechanical designs, multi-source data fusion, and flexible system expansion have become the current research trend. This paper reviews the research status and development trends, focusing on advanced mechanical design, precise control, and adjustability: (1) innovative mechanical structures, including seeding unit, driving system, and sensing system of precision seeders; (2) precision control technologies, including seed spacing, seeding depth, trajectory control, and monitoring; (3) flexible system extensibility, such as multi-crop adaptability and multi-functional integration. After systematically reviewing and analyzing these technologies, this paper further discusses the current development status, identifies existing challenges, and explores future trends in precision seeders, offering valuable insights for the advancement of precision seeders and the intelligent transformation of agriculture.