In response to the challenges of reduced efficiency and compromised seeding accuracy in conventional soybean planters operating at high speeds, this research introduces a novel precision seeding system powered by an electric drive, aiming to enhance both operational reliability and sowing precision. The entire system is powered by the tractor’s 12 V battery and incorporates an OGWO-Fuzzy PID control strategy to regulate the seeding motor speed. To achieve faster and more accurate regulation of the seeding motor speed, this study employs a ternary phase-diagram-based strategy to optimize the weight allocation among the α, β, and δ wolves within the Grey Wolf Optimization (GWO) algorithm. Based on engineering requirements, the optimal weight ratio was determined to be 16:2:1. Simulation results indicate that the optimized OGWO-Fuzzy PID control strategy achieves a settling time of only 0.17 s with no overshoot. In bench tests, the OGWO-Fuzzy PID control strategy significantly outperformed both GWO-Fuzzy PID and Fuzzy PID in terms of seed-metering speed regulation time and accuracy. The average qualified seeding index reached 95.68%, demonstrating excellent seeding performance at medium-to-high operating speeds. This study provides a practical and technically robust approach to ensure seeding quality during medium–high-speed soybean planting

To improve the planning efficiency of the Hybrid A-Star algorithm in facility-based agriculture environments, we propose a novel method that integrates directional guidance and segment-based heuristic weighting. Jump point search (JPS) is first employed to generate a reference path, from which key jump points and their heading angles are extracted to guide node expansion. The path is then segmented into straight and turning segments, each assigned dynamic heuristic weights to form a segment-wise heuristic sequence. Simulation results show that, while maintaining comparable path lengths, the proposed method significantly improves computational efficiency, reduces node expansion, and enhances overall path smoothness. These results highlight the proposed method’s effectiveness for path planning in autonomous mobile platforms operating within agricultural facilities.