This paper addresses the insufficient speed control accuracy observed in traditional seeding systems. This paper proposes an electric drive seeding control method that incorporates a composite control strategy combining the Rime optimization algorithm (RIME) with a backpropagation neural network (BPNN). Firstly, the architecture including radar/proximity switch dual-mode speed measurement, STM32F103 main control, and asymmetric half-bridge drive was constructed. Based on the kinematic model, a motor speed-plant spacing mapping relationship was derived to complete the selection of a brushless DC motor. Secondly, this study addresses the issues of large overshoot in traditional PID control, response lag in fuzzy PID, and local optima in BP-PID. To overcome these challenges, the RIME algorithm is employed to optimize the weight-updating mechanism of the backpropagation neural network (BPNN). The soft RIME search facilitates multi-directional exploration, while the hard RIME puncture enhances global optimization capability, significantly improving the adaptive accuracy of the parameters. The simulation results showed that the adjustment time of the proposed RIME-BP-PID in the step response is 73.8% shorter than the BP-PID, and the overshoot is reduced to 0.23%. The square wave tracking error is 27.8% of the traditional PID. The bench test was carried out at 6–12 km/h speed and 200–300 mm. The results showed that, compared with BP-PID, the qualified index of RIME-BP-PID increased by 1.67–1.94 percentage points, the missed seeding index decreased by 1.25–1.80 percentage points, and the coefficient of variation decreased by 4.90–5.82 percentage points. The algorithm effectively solves the problem of the strong nonlinear time-varying control of a seeding system and provides theoretical support for the research and development of precision agricultural equipment.

The dual drive sliding feed system can obtain a uniform and stable resolution at extremely low speeds and significantly reduce the system’s nonlinear friction. However, the numerous thermal sources within the system and the multipoint sliding contact during transmission result in a significant temperature rise, leading to considerable thermal deformation and errors. Moreover, the responsive mechanism of the thermal characteristics needs to be clarified. Therefore, firstly, a frictional torque model of the engagement of the screw and nut is established, and the heat generation, heat transfer, and thermal contact resistance (TCR) are solved. Then, based on the solution, a finite element thermal simulation model of the dual drive sliding feed system is established, and experiments are performed for validation. The results show that the error in temperature at the measuring point is less than 2.1 °C, and the axial thermal elongation of the screw is less than 6.2 µm. Finally, the thermal characteristics of the feeding system under various operating conditions are analyzed. The results show that the established thermal simulated model can effectively describe the dynamic thermal characteristics of the dual drive sliding feed system during operation. The effects of the rotational speed and ambient temperature on the dynamic thermal characteristics of the dual drive sliding feed system are investigated separately. The temperature increase in each part of the screw during the operation is characterized.

To address the problem of decreased rotational speed tracking accuracy of the seeding motor caused by system nonlinearity, time-varying parameters, and field disturbances during high-speed electric-driven precision seeding, a PINN-PID control method was proposed. First, the kinematic relationship among operating speed, target plant spacing, and seeding motor rotational speed was established, and a motor dynamics model was constructed. Subsequently, the data supervision error, seeding kinematic residual, motor dynamics residual, and smoothness constraint were jointly embedded into the neural network loss function, and the network prediction results were combined with PID feedback regulation. Test results showed that the RMSE and MAPE of PINN rotational speed prediction are 0.7697 ± 0.0422 r/min and 0.640 ± 0.040%, respectively, with a coefficient of determination R² of 0.99714 ± 0.00047. In simulation tests, the step response overshoot and settling time of PINN-PID were 0.17% and 0.13 s, respectively, which were 66.28% and 63.89% lower than those of BP-PID. In bench tests, PINN-PID achieved a 97.41% mean qualification index, gaining 3.56, 2.13, and 0.79 percentage points over conventional PID, fuzzy PID, and BP-PID, respectively. Constant-speed field trials yielded 95.13% qualification and 13.90% coefficient of variation; under stepped-speed conditions, the values were 95.37% and 14.05%, representing a 2.00-percentage-point qualification gain and a 6.49% coefficient of variation reduction versus BP-PID. Fusing physical constraints with data-driven learning improved motor state prediction and disturbance adaptability, offering a reliable, physically interpretable control approach for high-speed electric precision seeding.

Accurate weld keypoint localization is an important visual perception task for robotic welding, weld tracking, and intelligent manufacturing. However, weld keypoints in line-structured light images are usually small, weakly textured, and easily affected by reflections, noise, and spurious laser stripes. These factors make it difficult for lightweight detection models to balance localization accuracy and computational efficiency. To address this problem, this paper proposes Weld-FHG-YOLO, a lightweight multi-frequency feature fusion network for weld keypoint localization. The proposed model is built on the You Only Look Once version 11 nano (YOLO11n) framework and focuses on optimizing feature fusion and scale transformation in the Neck. Specifically, FasterC3K2 is introduced to replace the original C3K2 modules in the Neck, thereby reducing redundant computation during multi-scale feature fusion. In addition, a Haar Wavelet Decomposition and Group Shuffle Convolution (HWD-GSConv) downsampling fusion module is designed, in which Haar wavelet decomposition preserves low-frequency structural information and high-frequency details, while GSConv performs lightweight multi-frequency feature fusion. Experimental results show that Weld-FHG-YOLO achieves 2.301 M parameters and 6.016 GFLOPs, which are 11.26% and 6.83% lower than those of YOLO11n, respectively. Meanwhile, mAP@0.5:0.95 increases from 0.7337 to 0.7901, the Mean Center Error (MCE) decreases from 2.254 px to 2.131 px, and the CPU inference speed increases from 13.69 to 14.77 frames per second (FPS). These results indicate that the proposed method improves strict localization accuracy and localization stability while maintaining a lightweight computational profile, providing a practical visual perception approach for weld keypoint localization in resource-constrained intelligent manufacturing scenarios.

Conventional maize fertilization suffers from uneven distribution, fertilizer waste, and environmental pollution. To address these issues and achieve precision fertilization for maize, a straight grooved-wheel fertilizer apparatus (SGWFA) was designed and optimized using the discrete element method (DEM). The blocking characteristic of the SGWFA was also evaluated. The optimal configuration (eight grooves, inner diameter of 26 mm) yielded a minimum discharge uniformity coefficient of variation of 2.50% and mild blocking, with a maximum total force of 161.884 N. Furthermore, a nonsingular terminal sliding mode control (NTSMC) algorithm was proposed for the speed loop of the brushless DC (BLDC) motor drive, while the current loop used conventional proportional-integral (PI) control. The overall system achieved dual closed-loop speed and current regulation with finite-time convergence of the speed tracking error. Simulations showed that, compared with conventional PI and fuzzy PI controllers, NTSMC had the smallest overshoot of 3.4%, the shortest settling time of 0.165 s, and the fastest disturbance rejection. Bench tests confirmed that the coefficient of variation under NTSMC was 2.85%, markedly better than fuzzy PI’s 3.15% and conventional PI’s 4.03%. It is also basically consistent with the simulation results. Field tests at 6, 9, and 12 km/h demonstrated over 95% per-row fertilization accuracy, with a maximum relative error of only 4.61%. This integrated system can effectively achieve precise fertilizer application under variable field conditions.