Yongjia Sun

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.

Autonomous mobile robots are playing an increasingly significant role in modern smart orchards by supporting precision agricultural operations such as target-oriented spraying and autonomous harvesting. Nevertheless, achieving high-precision trajectory tracking and stable motion in complex, unstructured orchard environments remains challenging, because tracking deviations induced by uneven terrain and low-traction soil can directly affect operational safety and efficiency. To address this challenge, the present study proposes an adaptive tracking controller which integrates model-driven and data-driven approaches. Firstly, a six-state planar dynamic model based on Newton–Euler equations is established to describe motion characteristics. Secondly, an improved Particle Swarm Optimization (PSO) algorithm is employed for offline parameter optimization under representative operating conditions. The process thus engenders a mapping dataset that relates the real-time motion states of the orchard mobile robot to the optimized horizon parameters and weights. Finally, an Adaptive Neuro-Fuzzy Inference System (ANFIS) is trained using this dataset, enabling adaptive adjustment of MPC parameters according to the robot motion state. Simulation and experimental results demonstrate that, in Double-Lane-Change (DLC) and serpentine simulations, the proposed controller reduced lateral and heading Root-Mean-Square (RMS) errors to 0.0109 m/0.0081 rad and 0.0102 m/0.0117 rad, achieving reductions of 49.30–85.58% and 68.60–88.02% compared with Pure Pursuit, Stanley, Linear Quadratic Regulator (LQR), and traditional MPC, respectively. In orchard field tests with circular and Figure-8 trajectories at 0.3–0.6 m/s, the lateral RMS errors were recorded as 0.0112–0.0182 m and 0.0156–0.0262 m, respectively, corresponding to reductions of 46.94–61.52% relative to traditional MPC, while the heading RMS error remained below 0.0510 rad. These findings substantiate the efficacy of the proposed controller in enhancing the accuracy and adaptability of the system, thereby providing a resilient and precise control framework for operation within orchard environments.

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.

Air-assisted boom sprayer is proven to be one of the best pesticide application methods to achieve uniform deposition of droplets in the canopy and improve the effective utilization of pesticides. However, the air flow velocity, air flow volume and air flow direction of the orchard sprayer should match the characteristic parameters of the target canopy, equipment spraying parameters and meteorological conditions so as to improve the spraying quality and reduce environmental pollution. This paper elaborates on the research status of air-assisted field sprayers and orchard sprayers, summarizes the research methods of air-assisted sprayers in four aspects, including experimental verification, theoretical analysis, simulation and structural optimization, and clarifies the advantages and disadvantages of these methods. It also presents two future research and development trends, including the intelligent, precise dynamic regulation of air flow velocity, air flow volume and air flow direction and the instant feedback of spraying quality, hoping to provide a reference for the research of air-assisted spray technology and equipment.