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.

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.