This paper established an accurate discrete element for ultrasonic vibration-cutter-soil interaction model to study the interaction mechanism between the soil-engaging component and the soil. In order to reduce the interaction between calibration parameters and improve the calibration accuracy, it is proposed that the soil constitutive, contact parameters, and bonding parameters be calibrated by combining the soil repose angle experiment and the soil resistance experiment of ultrasonic vibration cutting. The study adopts the Hertz-Mindlin (no slip) contact model used in EDEM, to explore soil particle interactions. The central composite design is used to achieve systematic investigation. 3-factor 3-level orthogonal design experiment was employed using the coefficient of restitution, the coefficient of static friction, and the coefficient of rolling friction as key test factors and soil’s repose angle as the response index. Based on the Hertz-Mindlin with bonding contact model, Design-Expert 13.0 software was used to design the Plackett-Burman experiment, the steepest ascent, and the Box-Behnken experiment. With the maximum soil cutting resistance in ultrasonic vibration cutting experiment used as the response value, the adhesion parameters were optimized, and the optimal solution combination was obtained as: Normal Stiffness = 4.635 × 10⁶ N/m, Shear Stiffness = 3.401 × 10⁶ N/m, and Bonded Disk Radius = 2.57 mm. The optimal parameter combinations obtained from the calibration experiments were verified in two ways: ultrasonic vibration cutting and non-ultrasonic vibration cutting. The results showed that the errors between the simulation values and the actual values of the two comparative experiments were less than 5%, and the model calibrated for the three parameters can be used to study the drag reduction mechanism of ultrasonic vibration cutting in soil.

Precise positioning of clamping-points is the core and difficulty of realizing fully automated grafting of plug pepper seedlings. Traditional mechanical positioning methods often struggle to accommodate the morphological variations of pepper seedlings across an entire plug tray, resulting in large positioning errors and high clamping failure rates. To address this problem, this study develops an improved YOLOv8n-based framework for accurate detection and spatial positioning of seedling clamping points. The baseline YOLOv8n is optimized by integrating the SimAM, RFAConv and WIoU loss function to establish an enhanced SRW-YOLOv8n model. Moreover, a shielding-supporting mechanism and structured image processing strategy are adopted to suppress dense seedling interference, and depth camera calibration is applied to convert pixel coordinates into 3D spatial coordinates. Experimental results show that the SRW-YOLOv8n achieves 96.6% precision, 98.4% recall, 97.5% F1 and 97.4% mAP@0.5, outperforming the original YOLOv8n. The proposed system delivers average absolute positioning errors of 2.49 mm, 2.39 mm and 1.83 mm in the x, y and z axes, fully satisfying high-precision grafting requirements. This method provides robust spatial positioning guidance for automated pepper seedling grafting operations.

To address unstable seed filling and susceptibility to seed damage in air-suction peanut seed-metering devices, a flexible cell-hole seed-metering device comprising a double-layer seed-metering disc, elastic rubber cell-hole inserts, and conical springs was developed. EDEM simulations, bench tests, and field tests were conducted to investigate the effects of cell-hole type, cell-hole height, and seed-metering disc speed. Seed velocity, force, and disturbance transmission characteristics were analyzed in the simulations, while seed-metering performance was evaluated using the miss-seeding, multiple-seeding, and seed-damage indices in the bench tests. The results showed that the single ellipsoid cell-hole type produced greater consistency between the directions of seed motion and cell-hole movement. A cell-hole height of 5 mm provided an effective balance between seed disturbance and damage reduction, while a seed-metering disc speed of 20 r/min improved seed-adsorption stability. In the field tests, the mean miss-seeding, multiple-seeding, and seed-damage indices were 3.75%, 4.54%, and 1.75%, respectively. These findings demonstrate that flexible cell holes can guide seeds to move with the cell holes and promote the stable transmission of localized disturbance throughout the seed mass, thereby providing an effective approach to improving seed-adsorption stability while limiting mechanical damage.

Traditional electric-drive maize seed-metering systems, when operating at high speeds, are constrained by insufficient control accuracy and sluggish dynamic response, making it difficult to guarantee qualified single-seed placement rates, which directly affects field sowing quality and subsequent maize yield. To address the inherent shortcomings of conventional fuzzy PID control—namely, lagging manual parameter tuning and the susceptibility of standard particle swarm optimization to local optima—this paper proposes an improved particle swarm optimization algorithm. By introducing adaptive inertia weight and chaotic mutation operators to optimize the iterative mechanism of the particle swarm optimization, precise tuning of fuzzy PID control parameters is achieved. Simulink simulations show that the improved algorithm reduces the optimal fitness value by 60.66% compared with the standard algorithm, and increases the convergence speed by 30%. Bench test results confirm that, under operating speeds of 10–14 km/h, the proposed control strategy exhibits superior dynamic and steady-state performance; at the high operating speed of 14 km/h, the seeding quality index and coefficient of variation in plant spacing are 91.2% and 10.8%, respectively, representing significant improvements over PSO-Fuzzy-PID (88.1%, 12.8%) and conventional Fuzzy-PID (83.2%, 16.2%). This study can provide an important theoretical basis for precise control of high-speed maize seeding.

During ultrasonic detection of buried seeds after sowing, variations in soil conditions can strongly affect ultrasonic signals and make seed identification difficult. In this study, soil moisture content, particle size, and compaction pressure were investigated to evaluate their effects on ultrasonic frequency-domain responses and seed-presence discrimination. The first-wave dominant frequency (F_g) and dominant-frequency amplitude ratio (k) were extracted from the received signals. Seed presence altered the spectral peak position and amplitude distribution, whereas the direction of the F_g shift varied with soil conditions, limiting its use as a standalone discrimination feature. In sieved soil, k showed the best discrimination at 10% moisture content, with an AUC of 0.951. Using the predefined threshold k = 1, the sensitivity, specificity, and overall accuracy were 94.7%, 81.3%, and 88.0%, respectively. In non-sieved field soil, the highest AUC was 0.923 at 15% moisture content, with an accuracy of 90.0% using k = 1. An additional 40 independent tests conducted at 15% moisture content and 0.50 MPa yielded a sensitivity of 95.0%, a specificity of 90.0%, and an overall accuracy of 92.5%. These results demonstrate the potential of ultrasonic frequency-domain features for identifying buried seeds under controlled soil conditions.

To address the difficulty of stable axial orientation determination of cucurbit rootstock seedlings under random initial postures, true-leaf interference, and mechanical occlusion during mechanized grafting, a two-stage axial orientation recognition and rotational correction method integrating mechanical constraint and machine vision was proposed. In the first stage, seedling posture was normalized using the PCA axis of the whole green region, and the seedling was rotated to avoid the leaf hold-down ring handle. In the second stage, mechanically occluded leaf regions were restored after ring constraint, and a minimum-span half-area window method based on polar-angle distribution was proposed to determine the directed cotyledon axis. The method was compared with the rotation-center–cotyledon-centroid line method and the single-cotyledon PCA method. Fifty seedlings were tested at each of the true-leaf emergence, initial true-leaf expansion, and true-leaf extension stages. In 150 first-stage rotation trials conducted on 50 seedlings, the absolute PCA-axis deviation after rotation was within 1.5° in every trial. In a separate handle-avoidance test, the avoidance rate was 100%. In the second stage, the proposed method yielded median absolute errors of 0.71°–1.26° for both cotyledons, with no significant differences among growth stages. In contrast, the errors of the centroid-line and PCA methods increased significantly with seedling development, reaching maximum median errors of 5.98° and 19.22°, respectively. After the second-stage rotation, the algorithm-derived residual axial deviation of the right cotyledon was below 1° for all 150 seedlings. These results indicate that the proposed method provides stable cotyledon-axis recognition under the investigated experimental conditions.