Abstract: Abstract: As one of the main parameters for evaluating mechanized land leveling technology, which is the basic link of modernization and precision agriculture, soil surface roughness is also a factor that must be considered for field management such as crop planting, irrigation, harvesting and so on. However, a rapid and low-cost accuracy measurement method is absent currently. In this work, we proposed a method for rapidly evaluating soil surface roughness. The soil surface roughness was assessed according to the images generated by a self-design system based on line structure laser light principle. The parameters of the system was determined by a preliminary experiment. The accuracy of the system was 0.126 mm. Considering the requirements by common crops such as oilseed rape, potato, rice and vegetables, we screened the soils with 4 aperture sieves. The obtained soil particle size was higher than 2 mm, higher than 0.9 mm and not less than 2 mm, higher than 0.3 mm and not less than 0.9 mm, and not higher than 0.3 mm. By the self-design system, soil samples images were obtained. After segmentation, morphological processing and edge detection, the images of prepared soil samples were processed for extracting the center sub-pixels of structured light stripe and the sub-pixels were combined together for obtain the 3D point cloud of each sample. With delaunay triangulation algorithm, the 3D surface modeling of the soil surface was established and the curve surface area was calculated. The ratio of curved surface area to surface area was calculated in order to characterize the flatness of the soil surface. The threshold values of the ratio for determining the sample surface roughness were determined by analyzing the ratio values of 4 samples with different particle sizes. The larger ratio values indicate more rough soil surface. Based on the ratio, soil was considered as relatively flat, relatively dense, relatively rough and rough when the difference of ratio and 1 was 0, higher than 0 and not higher than 0.5, higher than 0.5 and not higher than 1, and higher than 1. The method was validated by soil bin test. The soil samples in soil bin with treatments in uncultivated, shallow rotary tillage and deep rotary tillage conditions were used for the validation of the system and algorithm. Meanwhile, our method was compared with probe method. The results showed that the ratio values of surface samples with uncultivated, shallow rotary tillage and deep rotary tillage increased gradually. The ratio was 1.214, 1.633 and 1.939 for the samples in uncultivated, shallow rotary tillage and deep rotary tillage conditions, respectively. The change of the ratio was consistent with that of the standard deviation of relative height difference of sample surface by the probe method. Based on the ratio, the sample surface in the treatments of uncultivated, shallow rotary tillage and deep rotary tillage was considered as relatively dense, relatively rough and relatively rough, respectively. The results were consistent with the actual characteristics of the samples. These results indicated that the presented measurement method could be applied in soil surface roughness assessment.