## 3 Results  
The vegetation indices were used for preliminary assessment of the plant samples at the booting stage. This method was previously proposed by researchers for the monitoring of vegetation stages using RGB data and drones [22]. The VARI provides an estimate of the proportion of vegetation that minimally responds to atmospheric factors, which can greatly affect crop yield [23]. Compared with the standard Moskovskaya 56, variety 187 turned out to be the most unstable and responsive to external factors, and varieties 150\(\times\) and 184\(\times\) were the least responsive. The VIgreen vegetation index, which uses an RGB indicator to analyze and classify vegetation canopy, can identify phenological changes during a distinct period. Compared to the standard, the variety sample 187 was the most diverse in three replicates according to this index, and the variety sample 184h was the least diverse. The GLI was developed to assess the impact of cereals on grazing, as well as to establish the amount of chlorophyll in rice leaves and to detect tree diseases. Compared with the standard, variety 187 was maximally resistant to grazing and contained the maximum amount of chlorophyll according to this index, while variety 184h had the minimum index value (Figure S3). Analysis of the kernel weight showed that the control variety Moskovskaya 56 (average weight 0.0562 \(\pm\) 0.0099 g) and lines 187h (0.0582 \(\pm\) 0.0113 g), 188h (0.0561 \(\pm\) 0.0089 g) and 184h (0.0553 \(\pm\) 0.0090 g) united into a group of samples with the highest grain weight (Figure 1A), area and perimeter. Line 150h occupies an intermediate position between the groups of lines with medium- and high-weight values. And lines 151h and 152h differed from others as samples with low grain weight (0.0464 and 0.0463 g, respectively). The coefficient of variation in grain weight is in the range of 16-19% for all samples in this study. Figure 1: Distribution frequencies of the evaluated traits in winter soft wheat of the Moskovskaya 56 variety and some promising lines. Legend: (**A**)—kernel weight; (**B**)—mean asymmetry index; (**C**)—kernel length; (**D**)—kernel width. Letters show the comparison of arithmetic means by the one-way analysis of variance with [PERSON]’s post hoc test (\(p=0.05\)). To calculate the kernel roundness indices, length and width measurements were used, which were specific characteristics for the samples studied (Figure 1C,D). The closer the roundness index is to 1, the more pronounced the roundness of the grain shape, and vice versa; the distance from 1 leads to a more elongated shape (Figure S2). Since the ideal shape for kernel processing is round, among our samples, the best index is at 150h (0.61), then at the group 152h, 151h, 171h (0.57, 0.58 and 0.58, respectively). The most elongated shape is at 188h, 187h and Moskovskaya 56 (0.51, 0.51 and 0.52, respectively). The variation coefficient of this feature is at the level of 8-11% (Figure 2). Accordingly, the range of variability of the kernel length is from 6.6 mm for 150h to 7.91 mm for 188h, and the kernel width is from 3.77 mm for Moskovskaya 56 to 4.11 mm for 152h. The coefficient of variation in the kernel length and width does not exceed 10%. The average index of transverse cut asymmetry in kernel shows that the kernels of line 188h are the most symmetrical (Figure S2). The average index of the ratio of the left and right sides of kernel in this line is close to the ratio of 50:50% and is 0.514, i.e., the ratio of the left and right sides is 51:49% (Figure 1). When breeders plan schemes of crossings to obtain greater splitting by a specific trait, it is necessary to select samples with a higher value of the coefficient of variation in the trait. Accordingly, if it is necessary to conduct targeted selection for a specific value of the trait, then variety samples with low variation are selected.