The accurate determination of discrete element parameters is crucial for ensuring reliable results in simulating the critical post-harvest stages of rice grain (processing, transportation, and storage) with different moisture contents. To determine the discrete element parameters, a physical model of rice grain was constructed by the multi-sphere (MS) modeling approach. Using the repose angle as the evaluation index, the discrete element parameters of rice grain were calibrated and optimized through the Plackett–Burman (PB) test, the steepest climbing test, and the Box–Behnken (BB) test using EDEM software. A moisture content–significance discrete element parameters model was further developed based on a moisture content–repose angle model ($R^2$ = 0.992) and a repose angle–significance discrete element parameters model ($R^2$ = 0.970). The calibration results showed that the relative error between the simulated and actual repose angle did not exceed 3.52%. Meanwhile, the cylinder lifting method and unloading mass flow rate verification were performed. And the results showed that the relative errors of the repose angle and mass flow rate of rice grain did not exceed 2.09% and 7.72%, respectively. The study provides a general and reliable method for determining the parameters of discrete element method simulation for rice grain with different moisture contents.

Cracking in paddy during processing and storage can significantly degrade its quality and economic value. This study elucidates the crack formation law in paddy across various drying stages through experiments and simulations that integrate multiple physical fields. A predictive model for crack occurrence, based on the relationship between the probability of cracking and drying time, is developed by introducing a critical moisture evaporation coefficient to delineate safe regio for cracking. The findings indicate a sharp increase in the percentage of drying cracks during the initial rapid drying phase, with continued escalation through the slower drying phase. The predictive model’s coefficient of determination exceeded 0.85, demonstrating its efficacy in forecasting crack progression. The primary driver for crack initiation and growth was identified as the cumulative effect of the moisture gradient, with the yield stress proving inadequate to counteract the stresses induced by this gradient, leading to cracking in paddy grains. These results furnish valuable insights for accurately predicting and managing crack development in paddy.