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.

Precision agriculture is a key trend in future agricultural development. Nevertheless, as a key crop in the global economy, cotton precision harvesting remains in its infancy. Therefore, this study proposes a method to precisely regulate cotton bale dimensions in packing cotton pickers, aiming to advance the precision harvesting of cotton. Firstly, based on the two basic assumptions, the relationship between the rotation angle of the rocker-arm component and the cotton bale dimension was derived. In addition, the one-to-one correspondence between the rotation angle and the dimension was demonstrated. Subsequently, a numerical case study was conducted to validate the method’s feasibility initially. However, solver failure occurred at larger angles, necessitating model refinement by incorporating the elastic deformation of the packing belt. Finally, a prototype was built and field tests were carried out. Based on the first field experiment results, the error caused by model simplification was compensated with quadratic polynomial least squares fitting. After compensating, the second field experiment results show that the absolute error in bale dimension remained below 49 mm and the relative error below 2.13%, as the diameter of the cotton bale was less than 2300 mm. This study lays a superior foundation for the precision harvesting of cotton.

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.