Hybrid approach improves seeding accuracy by 15–20% and reduces energy consumption in pneumatic seed drills, indicating enhanced operational performance.
The article presents the development of an intelligent control system for a pneumatic precision seed drill, based on a hybrid approach combining computational fluid dynamics (CFD) and machine learning methods. Numerical modeling of the air flow in the system was performed, operational parameters were optimized, and adaptive control algorithms were developed. The results show a 15–20% improvement in seeding accuracy, an 18–22% reduction in energy consumption, and enhanced system stability under varying operating conditions. The cost-benefit analysis confirms a payback period of 2–3 seasons. Future work focuses on integration with precision agriculture technologies and the development of cloud services for collaborative algorithm training.
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Kun et al. (2025) studied this question.
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