Threshing wheat (Triticum aestivum L.) at high speeds is the main reason behind abnormal seedlings and vigor reduction of the seeds. This problem is expected to be severe in head‐stripper combines with successive impact loadings of stripping and threshing units. The aim of this study was to simulate the effects of impact velocities (IV), number of impact loadings (NL), and seed moisture content (MC) on percentage of physical damage (PPD) and percentage of loss in germination (PLG) to wheat seeds. Modeling the correlation between dependent and independent variables was performed using mathematical and artificial neural networks (ANN). The result showed that all the three independent variables significantly influenced PPD and PLG (P = 0.01). Increasing the IV from 5 to 30 m s−1 caused an increase in PPD and PLG from 0.17 to 35.8% and from 0.37 to 19.9%, respectively. It was found that the seeds with higher MC could better withstand physical and physiological damage than those with lower MC. With an increase in NL from 1 to 3 times, the mean values of PPD and PLG were increased by 2.9 and 2.6 times, respectively. An ANN model with two hidden layers, trained with a back‐propagation algorithm, successfully learned the relationship between the input and output variables. In comparison with regression models, ANN performed better when predicting PPD and PLG to wheat seeds.

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Introduction Mechanical damage of seeds due to harvest, handling and other process is an important factor that affects the quality of seeds. Objectives To evaluate the impact damage to navy bean seeds. Methods The study was conducted under laboratory conditions, using an impact damage assessment device. Independent variables were: seed moisture content (10, 12.5, 15, 17.5, 20, and 25% wet basis), impact velocity (5, 7.5, 10, 12.5, and 15 m/s) and seed orientation (side and end). Results Impact velocity, moisture content and seed orientation were all significant at the 1% level on the physical damage in seeds. Increasing the impact velocity from 5 to 15 m/s caused an increase in the mean values of damage from 0.17 to 32.88%. The mean values of physical damage decreased significantly by 1.96 times (from 27.09 to 13.79%), with increase in the moisture content from 10 to 15%. However, by a higher increase in the moisture from 15 to 25%, the mean value of damage showed a non-significant increasing trend. It was found that the relationship between beans mechanical damage with moisture content and velocity of impact was non-linear and the percentage damage to seeds was a quadratic function of moisture content and impact velocity, respectively. Impact to the end of the seeds produced the higher damage (20.61%) than side of the seeds (11.14%). Conclusion To minimize physical damage to navy bean seeds, the impact velocity should be limited to 10 m/s or below. The optimum level of moisture, where impact damage was minimized, was about 15%.

The objective of this research was to evaluate and model the mechanical damage to corn seeds under impact loading. The experiments were conducted at moisture contents of 7.60 to 25% (wet basis) and at the impact energies of 0.1, 0.2 and 0.3 J, using an impact damage assessment device. The results showed that impact energy, moisture content, and the interaction effects of these two variables significantly influenced the percentage of physical damage in corn seeds (p<0.01). Increasing the impact of the energy from 0.1 to 0.3 J caused a significant increase in the mean values of damage from 23.73 to 83.49%. The mean values of physical damage decreased significantly by a factor of 1.92 (from 83.75 to 43.56%), with an increase in the moisture content from 7.6 to 20%. However, by a higher increase in the moisture from 20 to 25%, the mean value of damage showed a non-significant increasing trend. There was an optimum moisture level of about 17 to 20%, at which seed damage was minimized. An empirical model composed of seed moisture content and energy impact was developed for accurately describing the percentage of physical damage to corn seeds. It was found that the model has provided satisfactory results over the whole set of values for the dependent variable.