Analysis reveals multi-objective optimization improves cutting efficiency in automated seed cutting machines, suggesting machine vision may enhance adaptability.
Current potato seed cutting processes suffer from low automation and poor cutting quality, primarily due to the lack of accurate and adaptive cutting mechanisms. This study presents the first application of a Delta parallel manipulator for automated potato seed cutting. A high‐speed cutting apparatus was designed, modeled, and experimentally validated. The key physical and mechanical properties of the “Holland 15” variety were first measured to establish the design basis. Comprehensive kinematic and simplified dynamic models were then developed to verify the manipulator's theoretical feasibility. A Response Surface Methodology (RSM) based on a Box–Behnken Design (BBD) was employed to analyze the influence of three key factors—conveyor speed, cutting speed, and belt tension—on system performance, characterized by cutting efficiency and qualification rate. Analysis of Variance (ANOVA) confirmed that both second‐order models were statistically significant ( p < 0.0001). Multi‐objective optimization determined the optimal parameters: 0.25 m/s conveyor speed, 2.5 m/s cutting speed, and 15 N belt tension. Verification experiments under these conditions achieved an average cutting efficiency of 417.52 kg/h and a qualification rate of 89.56%, with prediction errors below 2%, validating the model's high accuracy and reliability. Although the current system performs excellently under laboratory conditions, its robustness under variable field environments and across different potato varieties remains to be verified. Future work will integrate machine vision for adaptive bud‐eye detection, extend testing to multiple cultivars, and assess industrial scalability. The study confirms the feasibility and superiority of Delta‐based dynamic cutting, providing a solid technical foundation for intelligent seed‐cutting equipment.
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Huang et al. (2025) studied this question.
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