The seedling retrieval mechanism is a crucial component of fully automatic transplanting machines, significantly influencing the quality, reliability, and efficiency of the transplanting process. Nonetheless, the existing seedling retrieval mechanisms in current transplanting machines exhibit several deficiencies, including substantial damage to seedlings and inadequate retrieval accuracy. To overcome these challenges, we propose an integrated approach combining pneumatic and mechanical techniques to further improve performance. By employing a lower thimble elevation and clamping mechanism, alongside a mathematical model based on the seedling removal process, this method ensures precise seedling extraction and minimizes damage to the root system and substrate. The novelty of this study lies in its ability to reduce the adhesion between seedlings and the holes of the plug plate, thereby minimizing non-destructive extraction of the seedlings and preserving the integrity of the matrix, which is essential for ensuring healthy seedling growth. Moreover, the optimization of the seedling retrieval trajectory enhances the accuracy of the seedling retrieval mechanism while also meeting the requisite speed requirements. Experimental results indicate that at a rate of 72 seedlings per minute, the extraction success rate reached 94.90%, and the casting success rate was 98.53%. The seedling injury rate was only 1.95%, resulting in an overall success rate of 91.69%. These findings confirm that the device meets operational efficiency requirements and delivers effective performance.

Despite some rudimentary handling vehicles employed in the labor-intensive harvesting and transportation of greenhouse vegetables, research on intelligent uncrewed transport vehicles remains limited. Herein, an uncrewed transport vehicle was designed for greenhouse solanaceous vegetable harvesting. Its overall structure and path planning were tailored to the greenhouse environment, with specially designed components, including the electric crawler chassis, unloading mechanism, and control system. A SLAM system based on fusion of LiDAR and inertial navigation ensures precise positioning and navigation with the help of an overall path planner using an A* algorithm and a 3D scanning constructed local virtual environment. Multi-sensor fusion localization, path planning, and control enable autonomous operation. Experimental studies demonstrated it can automatically move, pause, steer, and unload along predefined trajectories. The driving capacity and range of electric chassis reach the design specifications, whose walking speeds approach set speeds (<5% error). Under various loads, the vehicle closely follows the target path with very small tracking errors. Initial test points showed high localization accuracy at maximum longitudinal and lateral deviations of 9.5 cm and 6.7 cm, while the average value of the lateral deviation of other points below 5 cm. These findings contribute to the advancement of uncrewed transportation technology and equipment in greenhouse applications.

The compression molding performance of a substrate block has a significant effect on the quality and stability of mechanical transplanting. The physical experiment and DEM simulation were combined to evaluate the compression molding behavior of substrate block in this study. A calibration procedure of DEM parameters of peat particles was proposed at first. Then, the above parameters were brought into the contact model of the compression system–particles, and the effect of the loading speed on the compression behavior of the peat substrate block was investigated. The compressive force–displacement curves of the simulated and measured tests were all contained in the initial linear stage and non-linear stiffing stage. The particle number of central sections was higher than side section, and the variable coefficient was greater at higher loading speed. The substrate blocks all expanded after demolding. The higher the loading speed, the greater the expansion in the height’s direction, and the easier it was for cracks to be generated near the bottom. This study will provide a reference for the design of substrate block forming machines.

Design and test of the progressive push-out automatic seedling taking device for vegetable substrate block seedlings

Solid-liquid separation is essential for utilizing livestock and poultry manure, impacting greenhouse gas emissions and pollution control. This study aimed to address the high water content and low efficiency issues in screw extrusion solid-liquid separation technology. A screw separator was designed, and a Box-Behnken test scheme was used to optimize parameters with dairy manure as the test object. The study identified extender length, screw rotation speed, and counterweight mass as the influencing factors, with extrudate water content and solids extraction rate as evaluation indices. A quadratic regression model was established to describe the relationships between the influencing factors and evaluation indices. Optimal parameters were determined as: extender length=10 cm, screw rotation speed=55 r/min, and counterweight mass=34.73 kg. The predicted extrudate water content was 65.44 %, and solids extraction rate was 2.39 m³/h. Validation tests showed an average extrudate water content of 67.71 % and solids extraction rate of 2.10 m³/h, with relative errors of 3.3 % and 9.6 % respectively. The model's reliability was confirmed, providing a reference for designing livestock and poultry manure screw extrusion separators.

Accurate estimation of the effective operational area of automatic transplanters is essential for evaluating machinery productivity, improving field management efficiency, and supporting digital agricultural applications. However, operational-area estimation based on low-cost BeiDou trajectories remains challenging because raw trajectories are often affected by positioning uncertainty, trajectory discontinuities, and non-productive machine movements, including headland turning, road transportation, empty transfer, and seedling loading/unloading operations. Directly using all recorded trajectories may therefore introduce considerable area overestimation, whereas excessive trajectory filtering may remove valid productive operations. This study proposes a state-aware hierarchical trajectory fusion framework coupled with a low-cost BeiDou-based monitoring system for automatic operation-state recognition and operational-area estimation of transplanters. Unlike conventional trajectory-processing approaches that mainly focus on geometric smoothing or spatial clustering, the proposed framework jointly considers positioning reliability, motion consistency, spatiotemporal continuity, and operational semantics to extract productive transplanting trajectories from noisy GNSS observations. Three field datasets containing more than 49,000 trajectory points (approximately 30,000 valid records after preprocessing) were collected from automatic transplanters operating in Xinjiang, China. The proposed framework integrated adaptive trajectory filtering, motion-consistency screening, spatiotemporal continuity optimization, and operation-state recognition to classify five operation states, including field transplanting, headland turning, empty transfer, road travel, and seedling loading/unloading. After productive transplanting trajectories were identified, coordinate transformation was applied to convert the retained trajectories into a local coordinate system for operational-area estimation using the effective working width of 1.2 m obtained from overlap and pass-spacing correction of the nominal 1.4 m width. The estimated operational areas were validated against RTK-derived reference measurements. Across the three datasets, the proposed framework achieved F1-scores ranging from 0.94 to 0.95, trajectory continuity rates of 94.2–96.5%, and effective trajectory-length retention above 99.5%. The estimated operational-area bias was limited to 0.9–1.5%, compared with 12.8–18.4% obtained from direct calculation using raw trajectories. These results demonstrate that integrating motion consistency, spatiotemporal continuity, and operational-state recognition can effectively reduce operational-area overestimation caused by non-productive trajectories. The proposed framework provides a low-cost and interpretable approach for agricultural machinery operational monitoring and operational-area estimation. Although low-cost GNSS positioning alone cannot satisfy centimeter-level autonomous navigation requirements, the proposed approach provides a practical foundation for future integration with multi-sensor perception, edge-intelligent processing, and digital agricultural management systems.