Moisture-Dependent DEM Modeling and Optimization of Hole Formation and Seed Positioning for Precision Tomato Plug-Tray Sowing

Precision plug-tray sowing requires consistent hole geometry and seed placement, both of which depend on substrate moisture. This study developed a sequential discrete element method (DEM) framework that transfers the substrate particle configuration after punch retraction directly to seed dropping, enabling joint evaluation of hole quality and seed positioning. Equivalent spheres represented the substrate without explicit fibers or liquid bridges; effective contact parameters were calibrated using angle-of-repose (AoR) tests at five dry-basis moisture levels. Moisture content, punch cone half-angle, and pressing speed were jointly optimized by minimizing hole-depth deviation, equivalent hole-diameter deviation, hole-opening roundness deviation, and seed-center offset. For the selected calibration sets, simulated AoR differed from the measured targets by 0.62–1.13%. Using this parameterization, response-surface optimization estimated an optimum of 72.83% moisture, 46.30° cone half-angle, and 104.17 mm s−1 speed. Physical tests at the practical combination of 73%, 45°, and 100 mm s−1 gave respective response values of 0.94 mm, 0.45 mm, 0.100, and 0.57 mm, with EDEM-to-experiment errors of 2.13–8.89%. Comparisons at three selected combinations support joint process evaluation for the tested substrate and tray within the investigated ranges.