A Machine-Vision-Based Platform for the Automated and Integrated Measurement of Multiple Seed Physical Properties

Seed dimensions (length and width), surface color, frictional properties (static and kinetic coefficients of friction), thousand-seed weight, and angle of repose are five important categories of seed physical properties. Conventional methods generally measure these properties separately and rely heavily on manual operation, resulting in limited applicability and difficulty in balancing measurement efficiency and accuracy. To address these limitations, this study developed an automated method and an integrated platform incorporating automatic feeding, individual-seed positioning, state recognition, parameter acquisition, and cyclic control. After a single sample loading, the platform sequentially processed individual seeds, measured their dimensions, surface color, and static and kinetic coefficients of friction, and accumulated the measured seeds for subsequent thousand-seed weight and angle-of-repose determination, thereby enabling continuous automated measurement of the five categories of physical properties. Experiments were conducted using maize kernels, red kidney beans, and sunflower seeds to evaluate the measurement performance, repeatability, and cross-material adaptability of the platform. The standard deviations of repeated seed-dimension measurements were below 0.042 mm for all three seed types. The first moments of H and S in the HSV color space and a* and b* in the CIELAB color space were selected for color analysis and exhibited relatively low sensitivity to illumination variation under the tested imaging conditions. Within the stable-sliding intervals, the coefficients of determination for the relationship between actual seed displacement and squared time were all greater than 0.99, supporting the approximation of uniformly accelerated translational motion and the calculation of sliding acceleration for kinetic-friction determination. When the sample size used for thousand-seed weight estimation exceeded 50 seeds, further reductions in the coefficient of variation and average relative error were no greater than 0.2 percentage points. The angle-of-repose measurements distinguished differences in the pile formation characteristics of the three seed types. All three seed types completed the entire measurement procedure, corresponding to a platform adaptation success rate of 100%. In the cross-material validation, the coefficients of variation of all evaluated repeated-measurement indicators were no greater than 2.5%. These results indicate that, under the tested material and experimental conditions, the platform exhibited good operational stability, measurement repeatability, and cross-material adaptability, providing an effective approach for the automated and integrated measurement of multiple seed physical properties.