How Germination Changes During Individual Seed RGB-Space Differentiation: The Case of Pinus sylvestris L. сv. Negorelskaya

How Germination Changes During Individual Seed RGB-Space Differentiation: The Case of Pinus sylvestris L. сv. 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Negorelskaya Tatyana P. Novikova, Paweł Tylek, Arthur I. Novikov This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6639938/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract To watch the growth of 1200 P. sylvestris cv. Negorelskaya trees from seeds to young or even old stage is a big grant project. We want to make a «seed–culture» passport. Each individual seed (N = 1200) was weighed, and image acquisition via a flatbed scanner in the VIS wavelength region and seeded into an individual 120 cm 3 cell of a 40-cell container. On day 30, container-grown germination was evaluated according to the following dichotomous criterion: 1 – germinated (n 1 = 942), 0 – did not germinate (n 0 = 258), and 0-group and 1-group datasets were formed. The RGB space color of the individual seed epidermis between the 0- and the 1-group were compared via the Kolmogorov‒Smirnov criterion D. The lower individual weight of the seed in the 0-group compared with the 1-group was not accidental (p = 0.0045). Additionally, in the 0 group, the median values of R, G, and B brightness of pixels from individual seeds are not accidental (p = 0.0000381) compared with those of the 1 group. Therefore, in this experiment, seeds that reflected most of the light from the epidermis showed a lower germination when placed in the container. Biological sciences/Plant sciences/Plant physiology Biological sciences/Plant sciences Earth and environmental sciences/Ecology/Forestry Pinus sylvestris L. сv. Negorelskaya «seed–culture» technological passport individual seed coat color RGB container-grown germination agroforest landscape restoration Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The success of forest plantations, like protective forest strips 1 , 2 or field shelterbelts 3 , or seed orchards 4 , 5 , depends on how well the trees used can handle biotic (living) and abiotic (non-living) environmental factors. In turn, these characteristics are determined at the genetic level and determine the quality 6 of Forest Reproductive Material (FRM). As rightly noted, Clara Tattoni et al. 7 from the DAGRI: “Accurate estimates of forest seed production are central for a wide range of ecological studies 7 ”. The productivity of forest crops can be improved through breeding tests. The main purpose of the entire funded study was to track, evaluate, and analyze the individual reproduction cycles of 1,200 plants of Pinus sylvestris L. cv. Negorelskaya. We have created a «seed-culture» technological passport, starting from the datasets of the spectral and germination properties of each seed. A P. sylvestris cv. Negorelskaya is a breeding cultivar 8 characterized by intensive growth and early abundant seed production (up to 15%). All other things being equal, it's going to come down to the seeds quality 9 , 10 . The better the seeds 11 , the better the plants. And the seedlings quality 12 is closely linked to its seeds physical properties: seeds weights (or mass), seeds size 13 , seeds color – seed visible features 13 – 16 , seed infrared features 17 – 19 , seed immersion features 20 , seed irradiation features 21 , seed conditioned features 22 . All these studies were conducted on group seed samples. For example, weight was determined for 1,000 seeds, color characteristics were evaluated for 100 seeds, and seed germination was calculated for another 100 seeds. The individual mass of each seed and its individual visual spectral characteristics (seed coat color 23 – 26 , seed epidermis color, or pericarp color 17 ) are important indicators of seeds quality. The first result, according to Professor John A. Stanturf, is quite well known. The second result is interesting and may have some significance for quick seed sorting 27 . Seeds quality criteria 28 are also taken into account when designing seeds hoppers 29 , feeders 30 , graders 31 – 33 , and analyzers 34 , 35 , their electronic 36 and optoelectronic 37 components. Also, the success of a bunch of different seeding techniques, like aerial seeding 38 , 39 on hard-to-cultivate sites, depends on how good the seed is. Rational assessment of the effectiveness of reforestation technology 40 , 41 by P. sylvestris L. cv. Negorelskaya when moving seeds to the experimental site according to the climatic gradient, " dependence of accumulated precipitation on accumulated degree days " 42 , used in this study, is based on the hypothesis of the influence of spectrometric seed parameters on germination rates on day 30 43,44 in containers of an automatized forest nursery. The accuracy of nondestructive 33 , 37 , 45 detection of forest seeds via electromagnetic waves from different regions increases with the individual evaluation of each single seed 16 , 18 , 19 , 46 – 48 , which is affected by the focal distance 49 from the sensor to the point of reflection of the electromagnetic beam from the surface of the seed coat 50 . Moreover, depending on the type and nature of exposure to electromagnetic radiation, the biophysical 37 , 51 parameters of the seed may be correlated with different properties and indicators of a single seed: biochemical content of lipids 52 , 53 , starch 54 , protein 55 , trace elements 56 , etc., as well as physiological germination. The accuracy of detection also depends on the quality of the seed (size, shape, heterogeneity) and the characteristics of the detector. Esteve Agelet et al. (2014) conclude from a systematic search for 168 references for single seed identification that " although no measurement mode (reflectance, transmittance) have led to the best reported calibrations, when dealing with heterogeneous seeds reflection is the best working mode 57 ". In most studies of forest single seeds, expensive devices are used to create and detect electromagnetic radiation (VIS, NIR) reflected from cameras with prolonged exposure. Some researchers use rather expensive USB cameras with autofocus 58 , which are independent of the exhibition. However, the use of an inexpensive scanner 59 or a smartphone may be sufficient for the predictive evaluation of seedlots. To quantify the color of the seed coat in the VIS region of visible light wavelengths, Red‒Green‒Blue (RGB-space) is often used, for example, in Yuval Nehoshtan et al. 58 , Jaromir Przybyło and Mirosław Jabłoński 60 , which consists of estimating the medium color density (from 0–250) of an RGB image, as in Antonio Dell'Aquila 61 , or estimating the color density of each of the three color channels (R, G, or B), as in Érika Beatriz de Lima Castro et al. 62 , and Ruoyu Zhang et al. 63 . An express semantic analysis of keywords in the LENS system for seed research 14 , 27 , 64 – 67 on the basis of their images, organized according to an ORCID query by Clissia B. da Silva, returns the " germination " descriptor in a third of the cases. This finding is convincing enough to reveal a connection between the spectral characteristics of the seed epidermis and germination. The purpose of this paper was to evaluate the degree of relationship in the RGB space between the color of the seed coat of P. sylvestris L. cv. Negoretskaya and the seed quality determined by germination in the cells of an nursery container. 2. Results In this section, we demonstrate the results of 30-day germination of individual seeds (N = 1200) of Pinus sylvestris L. сv. Negorelskaya, depending on the individual RGB parameters of the color of the seed coat. All the considered RGB parameters (measured and normalized) differed significantly between the zero (1-group) and zero (0-group) germination groups (Table 1 ). Table 1 Variability of the measured and specific RGB color parameters of the seed coats of individual Pinus sylvestris L. cv. Negorelskaya seeds in groups with nonzero (1-group) and zero (0-group) container-based germination for 30 days after seeding. Patameter’s Title «1» - germination group (N = 942); mean | ±SD | CV% «0» - germination group (N = 258); mean | ±SD | CV% «1» vs. «0» P value mean’s different Kolmogorov‒Smirnov D Seed individual mass (m), g 0.006098 | 0.001672 | 27.42 0.005673 | 0.001961 | 34.57 0.0045 0.1226 Pixel brightness of the R-channel 86.42 | 23.81 | 27.55 109.10 | 31.33 | 28.71 < 0.0001 0.3540 Pixel brightness of the G-channel 75.96 | 16.68 | 21.96 92.60 | 23.30 | 25.16 < 0.0001 0.3372 Pixel brightness of the B-channel 69.51 | 12.31 | 17.71 81.28 | 18.02 | 22.17 < 0.0001 0.3017 Specific pixel brightness of the R-channel r = R/(R + G + B) 0.3690 | 0.0247 | 6.68 0.3820 | 0.0239 | 6.25 < 0.0001 0.3031 Specific pixel brightness of the G-channel g = G/(R + G + B) 0.3278 | 0.0062 | 1.89 0.3275 | 0.0055 | 1.67 0.0429 0.0973 Specific pixel brightness of the B-channel b = B/(R + G + B) 0.3031 | 0.0225 | 7.41 0.2905 | 0.0218 | 7.53 < 0.0001 0.3024 The mass parameter of an individual seed in both germination groups has a distribution at the tails that deviates slightly from the Gauss curve (Fig. 1, b); therefore, to evaluate the null hypothesis that there are no differences between the distributions of values in the groups, we apply the nonparametric Kolmogorov‒Smirnov D criterion. In both groups of seeds (with zero and nonzero germination), there is a moderate tendency toward low values of the individual weight parameter, since the medians (Me [Q1; Q3]) of the parameter values for the group are zero (5.5 mg [4.5; 7]) and nonzero (6.0 mg [5; 7]) germination is focused slightly to the left of the mean value (Fig. 1, a) and determines a slight left-sided asymmetry. The interquartile range of the IQR (Q3–Q1) parameter characterizes the nonzero germination group (2.0 versus 2.5 mg) as more robust to outliers. Moreover, the groups are characterized by an increased level (see the CV parameter in Table 1 ) of individual weight factor variability, and 27.9% of the seeds in group 1 (Fig. 1, c) and 22.1% of the seeds in group 0 (Fig. 1, d) have a mass of 6 mg. Nevertheless, it is statistically shown (Fig. 1) that the lower values of the individual seed weight from group 0 than those from group 1 are not accidental at a confidence level of p = 0.0045. The pixel brightness parameter R in the image of an individual seed in both germination groups has a distribution at the tails that deviates slightly from the Gauss curve (Fig. 2, b); therefore, to evaluate the null hypothesis of the absence of differences between the distribution of values in the groups, we apply the nonparametric Kolmogorov‒Smirnov criterion D. In the group of seeds with zero germination, there is a strong approximation of the median to the average value, namely, 109.5 [80.0; 134.3] to 109.1, but still on the right, which implies a minimal shift toward the prevalence of high values of R. In the group of seeds with nonzero germination, there is a moderate tendency of the R-parameter to low values, since the median of 80 [69; 99] parameter values is positioned on the left, in the middle between the mean value (Fig. 2, a) and Q1, which characterizes a moderate left‒sided asymmetry. The IQRs of the R parameter values characterize the nonzero germination group (30.0 vs. 54.3) as more resistant to outliers. At the same time, both groups are characterized by an increased level (see the CV parameter in the R-row in Table 1 ) of variability in the pixel brightness factor of the seed epidermis image in R-space; 24% of the seeds in the 1-group (Fig. 2, c) have an R-brightness of 70,12% of the seeds in the 0-group (Fig. 2, d) – R-brightness of 130. Nevertheless, it was statistically shown (Fig. 2) that the greater R-brightness of the epidermis of the 0-group seeds was not accidental (p < 0.0001). The distributions of the values of the G-brightness parameter of pixels in the image of an individual seed in the germination 1-group are moderate, and in the 0-group, they deviate significantly from the Gauss curve (Fig. 3, b); therefore, the nonparametric Kolmogorov‒Smirnov criterion D was used to evaluate the null hypothesis about the absence of differences between the distributions of values in the groups. In the 0-group of seeds, as well as in the R-brightness (see Fig. 2, a), there is a strong approximation from the median 91 [71.75; 111.3] to the average value of 92.6, but on the left, which implies a minimal shift toward the prevalence of lower values of G-brightness (Fig. 3, a). In the 1-group of seeds, there is a moderate tendency of the G-parameter to low values (as in the R-brightness), since the median 72 [65; 83] is focused on the left, almost in the middle between the mean value of 75.95 (Fig. 3, a) and Q1, which characterizes a moderate left-sided asymmetry. The interquartile range (IQR) of the distribution of G-parameter values characterizes the 1-group (18.0 vs. 39.55 for the 0-group) as more robust to outliers. Moreover, Group 1 is characterized by an average (21.96%), and Group 0 is characterized by an increased level (see the CV parameter in the G row in Table 1 ) of variability in the pixel brightness factor of the image of the epidermis of the seed in G space. Notably, 17.8% of the seeds in the 1-group (Fig. 3, c) presented a G-brightness of 70, and 10.9% of the seeds in the 0-group (Fig. 3, d) presented a G-brightness of 70. Nevertheless, it is statistically shown (Fig. 3) that higher values of G-brightness in the epidermis of seeds from the 0-group are not noticeable at a confidence level of p < 0.0001. The distributions of the pixel brightness parameter B in the image of an individual seed in germination group 1 are moderate, and in group 0, they deviate significantly from the Gauss curve (Fig. 4, b); therefore, the nonparametric Kolmogorov‒Smirnov D criterion was used to evaluate the null hypothesis that there are no differences between the distributions of values in the groups. In the 0-group of seeds, there is a moderate location of the median of 76.5 [66; 97] to the left of the mean value of 81.3, which suggests a moderate tendency toward the prevalence of lower values of B-brightness (Fig. 4, a). In the mean group 1, there is a moderate tendency of the B parameter to have low values (as in the R and G luminosities), since the median 67 [61; 75] is focused on the left, almost in the middle between the mean value of 69.5 (Fig. 4, a) and Q1, which characterizes moderate left‒sided asymmetry. The interquartile range (IQR) of the distribution of the values of the B parameter characterizes the 1-group (14 versus 31 in the 0-group) as more robust to outliers. In this case, group 1 and group 0 are characterized by the average (17.76 and 22.17%, respectively) level (see the CV parameter in the B-row in Table 1 ) variability of the pixel brightness factor of the image of the epidermis of the seed in the B-space. Notably, 22.6% of the seeds in group 1 (Fig. 4, c) have a B-brightness of 65, and 17.1% of the seeds in group 0 (Fig. 4, d) have a B-brightness of 65. Nevertheless, it is statistically shown (Fig. 4) that the higher values of B-brightness in the epidermis of the seeds from the 0-group are not random at a confidence level of p < 0.0001. Thus, for the 1 200 breeding seeds included in this study, it is statistically significant (p < 0.0001) that brighter seeds reflecting most of the light rays from the epidermis will have lower container germination. Visually, this can be represented as a palette on a black background of the median RGB brightness values for the two germination groups (Fig. 5). 3. Discussion We discuss the results of applying physical pixel brightness indices and their derivatives, normalized reflection indices, for potential evaluation of seed productivity: how they can be interpreted from the point of view of previous studies and working hypotheses. Note that the results and their significance should be interpreted within the framework of the experimental conditions. Image acquisition method as an interpretation criterion First, we note that obtaining an image of the epidermis of seeds should strive for the optimum, at which the cost of using devices would not exceed the productive efficiency of plants. In other words, the lowest resolution of the seed image and the minimum time of its exposure and processing, at which it is possible to detect statistically significant differences between the dichotomous groups of seed germination, would be preferable when choosing a phenotyping method in the future. The second fundamentally important factor is the R&D focus: one set of instruments and instruments is needed for scientific purposes, and another is needed for production conditions. The third factor determining the choice of statistical apparatus and the accuracy of the results is the level of certification of the sample: group – when a sample of seeds from 10 to 1000 or more pieces is selected as a single sample; individual – when a group of individual seeds is used as a single sample, each of which has a technological passport 68 , including morphometric and biophysical parameters and replenished as the seed germinates and the culture obtained from it grows early. In this study, we used a budget flatbed scanner available in almost every laboratory with a minimum image resolution of 300 dpi and obtained significant differences (p < 0.05) in the distribution of RGB spectral indices for germinating and nongerminating seeds of Pinus sylvestris L. cv. Negorelskaya, which is the continuation of research toward reducing the resolution of the scanner (for example, up to 150 dpi to search for the lowest possible resolution for identification). As in Yuval Nehoshtan et al. 58 , a color bodyless machine vision camera with an IDS UI-3884LE-C-HQ-AF liquid lens with a resolution of 6.41 megapixels (3088 × 2076 pixels), on the one hand, significantly increases the cost of seed detection, and on the other hand, it allows the assembly of an engineering and technically automated system with a controlled camera. For example, Guoqing Feng et al. reported that the accuracy of the dichotomous classification of wheat grains by health (healthy/infected with a fusarium) based on VIS images in the RGB space can reach 97% 69 , and Bernardes et al. 59 reported an accuracy of 99% when the Epson Perfection V800 flatbed scanner was used. RGB index as an interpretation criterion The following was accepted as the null hypothesis: the RGB index values in two dichotomous germination groups (0-germination and 1-germination) had a sufficiently high probability of being distributed equally. However, for all the RGB indices in this study, it is statistically significant (p < 0.001) that the high values of the indices in the 0-group compared with those in the 1-group are not accidental. This means that the biophysical criterion—the color of the seed epidermis—has great potential for predicting the physiological parameters of germination (that is, the manifestation of the phenotype). R. Zhang et al. 63 reported a regression relationship R 2 = 0.7148 (p = 0.01) for cotton seeds between the normalized parameters of color characteristics R/(R + G + B), G/(R + G + B), (R-G-B)/(R + G), and germination. Xu Yan et al. 70 developed software to simulate the sorting of seeds of five species in real time via the color ratios in RGB-space, which demonstrated a fairly high correlation when the purity of a seed batch was predicted. In the future, projects and collaborations based on these data descriptors are possible, for example, with a group of scientists led by C.B. Mastrangelo 59 , who will be aimed at developing informatization of forest management systems and will combine the ambitious goal of tracing and ensuring the ability to effectively manage the process of restoring forest landscapes “from seeds to forest crops” on the example of Scots pine ( P. sylvestris L.). In the future, we plan to expand research “from seeds to forest crops” to other types of woody plants. 4. Materials and methods 4.1. Seed collection Three samples (n = 400) of dewinged seeds were selected by quartering 30 from a seedlot of Scots pine ( Pinus sylvestris L. сv. Negorelskaya) collected in 2023 from a location (53.577939, 27.056128, 180 m asl). The authors confidently state that Pinus sylvestris L. сv. Negorelskaya is a cultivar. Detailed information on cultivar breeding was provided by Rabko et al. 8 . The information about this is confirmed by the certificate for variety No. 0003707 and the breeder's certificate No. 0005065, with priority dated 28.03.2008, issued to the author S.U. Rabko and colleagues by the Ministry of Agriculture and Food of the Republic of Belarus. The issue of italicizing the epithet Negorelskaya is quite debatable. On the one hand, the International Code of Nomenclature of Cultivated Plants (ICNCP) recommends against italicizing epithets below the species level. On the other hand, there are studies that provide fairly acceptable arguments for the use of italics throughout the taxonomic description (for example, Marco Thines et al. (2020) https://imafungus.biomedcentral.com/articles/ 10.1186/s43008-020-00048-6#Sec12 ). Therefore, the epithet Negorelskaya is written without italics. Currently, seeds of Scots pine ( P. sylvestris L. сv. Negorelskaya) for germination experiments in the gradation function 71 of the accumulated annual precipitation (mm) depending on the accumulated degree days 72 of the region. The current experiment is no exception: 1200 varietal seeds were removed from the collection site (1731 degree days, 722 mm) to the experimental site (2326 degree days; 786 mm). 4.2. Datasets creation The library of forest reproductive material (FRM-Library) 73 , which is based on Pravdin's conjecture 11 , 24 , is expanding and being filled with data on individual parameters and indicators of each single seed of P. sylvestris L. сv. Negorelskaya. Dataset blocks used in this study are presented in detail additional PDF file (Figure A1, Appendix A) and include: Seed morphometric block (from Dataset 1), included seed individual mass; Seed VIS-spectrometric block (from Dataset 2); Seed germination block (from Dataset 3. 4.3. Seed image processing Scanning, according to the proposed method of the author T.P. Novikova 68 (Patent application RU 2024137297, 2024-12-12), was performed for 40 seeds, placing them on flatbed scanner glass with a white background 16 in the order of future sowing in containers. We preconfigured the field size of the 40-seed scan by clicking the [Preview] button in the scan window of the scanner interface (Brother DCP). The scan paper size was cut to 280*145 mm. For the subsequent study of spectrometric properties and to ensure a sufficient level of subsequent segmentation of the image of the dorsal and ventral projections of the seeds of Scots pine ( P. sylvestris L. сv. Negorelskaya) with a square of 124 × 124 pixels, for example, as in Rodrigo K. Bernardes and coauthors 59 , a sufficient level of randomization, as well as minimization of the noise of the CCD scanner matrix, provided for the location of the seeds of Scots pine ( P. sylvestris L. сv. Negorelskaya) at a distance of at least 20 mm from the edge of the tablet in the order corresponding to the order of subsequent sowing of seeds in side-ingot containers. The scanning resolution was set to 300 dpi, the scanning mode was color, and the brightness was set to the default value. Moreover, the paper size in the pixels was set to 1718*3309 pixels. Next, the [Scan] button was pressed, and the scan timing was determined via a smart stopwatch background, the value of which was entered into the Excel table. For the period of sample scanning, the time from the appearance of the "Data Transfer" window to the appearance of a thumbnail of the scanned seed image in the left menu of the ABBYY Fine Reader program was assumed. The resulting seed scan was saved in uncompressed TIFF format with a file name of the form dI(1–40)@300 = Scan, where d(v) is the conditional dorsal (ventral) orientation of the seed relative to the scanner glass; I (II, III) is the number of random samples of seeds from the seedlot; 1–40 is the unique seed cipher in the current study; @300 (600,1200) is the scanning resolution, with dots per inch; and = Scan is the color of the reflective substrate of the scanner or colored paper. The resulting file (image) has the following numbering of each individual seed. After a sample of 40 seeds corresponding to the future location in side-ingot containers was scanned, the resolution and size of the paper were changed to 600 dots per inch and 3436*6619 pixels, respectively. Moreover, the timing was determined for a resolution of 600 dpi, and the data were entered into the corresponding cell of the Excel table. After the scan was saved, the resolution and paper size were changed to 1200 dpi and 6873*13238 pixels, respectively. 4.4. Seed germination processing The seeds (three samples of four hundred seeds each) were sown manually on June 23, 2023, into each of the 40 cells with a volume of 120 cm 3 in HIKO V-120 SideSlit containers (size 352*216*110 mm, 526 seedlings per square meter; BCC AB, Sweden). Each container was prefilled with an acid reaction peat substrate, and the seed was placed in the center of the cell at a depth of 0.5–1 cm. The location of the seeds for subsequent identification was carried out in accordance with Figure A.7, a ( supp;ementary ) indicating the initial reference cell from the outside with a special marker as in Figure A.7, b ( supp;ementary ) . After sowing 40 seeds, each container was filled with mulch in the form of perlite and placed on a pallet for transportation to the greenhouse. Each sample of 400 seeds was placed in 10 containers. Thirty 42 43 days after seeding, the individual germination of each seed was calculated (0 – not germinated; 1 – germinated). 4.5. Data analysis The pixel intensities of red, green and blue spectral visible channels (reflected at 700.0, 546.1 and 435.8 nm wavelengths, respectively 74 ) of the segmented individual seed image were analyzed via ImageJ ver. 1.46r open source software, such as P.D. Abeytilakarathna et al. 75 . For each seed in the zero and nonzero germination groups, the following RGB ratios were calculated via the Prism program, version 8.4.3: The distribution of indices and normalized RGB indices of segmented images of individual seeds in groups with nonzero and zero germination was visualized via the "box and whiskers" diagram in Prism, version 8.4.3. Moreover, the box size represents the interquartile range (IQR), with whiskers indicating the 10th‒90th percentiles. The index distribution was visualized via a frequency diagram, and the degree of normality of the distribution was visualized via a QQ plot. The level of variability of the individual seed weight parameters and RGB indicators of the seed coat was estimated on the basis of the values of the CV variation coefficient 76 , 77 : very low (less than 7%); low (7–15%); medium (16–25%); increased (26–35%); high (36–50%); and very high (more than 50%). To assess the significance of the differences between the distributions of the RGB indices in the zero and nonzero germination groups, the nonparametric Kolmogorov‒Smirnov test D at α = 0.05 was used. 5. Conclusions The lower individual seed weight in the 0-group than in the 1-group was not accidental (p = 0.0045). Additionally, in the 0-group, the median values of R, G, and B pixel brightness of individual seeds are not accidental (p < 0.0001) compared with those of the 1-group. Thus, for the 1,200 breeding seeds involved in this study, it is statistically significant to state that they are brighter (reflecting most of the light rays from the epidermis) and that the seeds will have a smaller container-grown germination. Declarations Supplementary Information: The online version contains supplementary material available at https://doi.org/10.1038/s... Author Contributions: Conceptualization, A.I.N. and T.P.N.; methodology, A.I.N. and P.T.; software, T.P.N.; validation, A.I.N. and T.P.N.; formal analysis, T.P.N., P.T., A.I.N.; investigation, T.P.N., P.T., and A.I.N.; resources, T.P.N., A.I.N.; data curation, T.P.N., A.I.N.; writing—original draft preparation, T.P.N., P.T., and A.I.N.; writing—review and editing, T.P.N., P.T., and A.I.N.; visualization, T.P.N.; supervision, A.I.N.; project administration, T.P.N.; funding acquisition, A.I.N. All the authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Russian Science Foundation (RSF), grant number 23-26-00228, https://rscf.ru/project/23-26-00228/. Acknowledgments: The authors acknowledge the Chair of Forest Plantations and Soil Science of Belarusian State Technological University (BSTU), for the opportunity to conduct research. The authors would also like to acknowledge the reviewers and the editorial board of the Scientific Reports journal for their valuable comments and recommendations, which have helped increase the reader's interest in the paper. Data availability statement : The seeds used in this study were collected in 2023 a forest seed orchard (Latitude 53.577939; Longitude 27.056128, Altitude 180 m a.s.l.; Negorelsky Experimental Forestry Center of Belarusian State Technological University, Minsk region, Belarus Republic). The original morphometric data—Dataset 1—of Pinus sylvestris L. сv. The individual Negorelskaya seeds (N = 1200) presented in the study are openly available in Mendeley Data at DOI : https://doi.org/10.17632/8g258nbgmf.1. The original VIS image data of Pinus sylvestris L. сv. The individual Negorelskaya seeds (N = 1200) presented in the study are openly available in Mendeley Data at DOI : https://doi.org/10.17632/dt78jhyw2j.2. The original germination data—Dataset 3—of Pinus sylvestris L. cv. The individual Negorelskaya seeds (N = 1200) presented in the study are openly available in Mendeley Data at DOI : https://doi.org/10.17632/hrs3fgc8tt.1. Competing interests: The authors declare no competing interests. References Novikov, A., Ivetic, V., Nikulin, S., Demidov, D. & Petrishchev, E. Frontier technique of creating protective forests stands around nurseries on inefficient sites: technological foundations. Eng. J. 12 , 115–125 (2022). Salugin, A. N., Kulik, A. V. & Uzolin, A. I. Stochastic Modeling of Effects Exercised by Protective Forest Strips: The Cauchy Distribution. Russ Agric. Sci. 47 , 328–332 (2021). Amichev, B. Y., Laroque, C. P. & Van Rees, K. C. J. Shelterbelt removals in Saskatchewan, Canada: implications for long-term carbon sequestration. Agrofor. Syst. 94 , 1665–1680 (2020). Kang, K. S. & Bilir, N. Seed Orchards (Establishment, Management and Genetics) (OGEM-VAK, 2021). Yardibi, F., Kang, K. S., Özbey, A. A. & Bilir, N. Bibliometric Analysis of Trends and Future Directions of Research and Development of Seed Orchards. Forests 15 , 953 (2024). Ivetić, V. & Novikov, A. I. The role of forest reproductive material quality in forest restoration. Eng. J. 9 , 56–65 (2019). Tattoni, C. et al. A comparison of ground-based count methods for quantifying seed production in temperate broadleaved tree species. Ann. Sci. 78 , 11 (2021). Rabko, S. U., Novikov, A. I., Novikova, T. P. & Petrishchev, E. P. Characteristics of the source material origin of Scots pine (Pinus sylvestris L.) sort «Negorelskaya». Bot. Issled . 54 , 213–225 (2024). Frischie, S., Miller, A. L., Pedrini, S. & Kildisheva, O. A. Ensuring seed quality in ecological restoration: native seed cleaning and testing. Restor. Ecol. 28 , S239–S248 (2020). Feng, L. et al. Hyperspectral imaging for seed quality and safety inspection: a review. Plant. Methods . 15 , 91 (2019). Novikov, A. I. Voronezh State University of Forestry and Technologies,. Improvement of technology for obtaining high-quality forest seed material : advanced Doctoral Thesis. (2021). Novikov, A. I., Rabko, S., Novikova, T. P. & Petrishchev, E. P. Dickson Quality Index: relation to technological impact on forest seeds. Eng. J. 13 , 23–36 (2023). Novikov, A. I., Sokolov, S. V., Drapalyuk, M. V., Zelikov, V. A. & Ivetić, V. Performance of Scots pine seedlings from seeds graded by colour. Forests 10 , 1064 (2019). Novikova, T. P. et al. The Root Collar Diameter Growth Reveals a Strong Relationship with the Height Growth of Juvenile Scots Pine Trees from Seeds Differentiated by Spectrometric Feature. Forests 14 , 1164 (2023). Novikov, A. I. & Ivetić, V. The effect of seed coat color grading on height of one-year-old container-grown Scots pine seedlings planted on post-fire site. IOP Conf. Ser. Earth Environ. Sci. 226 , 012043 (2019). Huang, B. et al. Applications of machine learning in pine nuts classification. Sci. Rep. 12 , 8799 (2022). Novikov, A. I., Drapalyuk, M. V., Sokolov, S. V. & Ivetić, V. VIS-NIR wave spectrometric features of acorns (Quercus robur L.) for machine grading. IOP Conf. Ser. Earth Environ. Sci. 392 , 012009 (2019). Tigabu, M., Daneshvar, A., Wu, P., Ma, X. & Christer Odén, P. Rapid and non-destructive evaluation of seed quality of Chinese fir by near infrared spectroscopy and multivariate discriminant analysis. New. For. 51 , 395–408 (2020). Tigabu, M. et al. Multivariate discriminant analysis of single seed near infrared spectra for sorting dead-filled and viable seeds of three pine species: does one model fit all species? Forests 10, article id 469 (2019). Dornyak, O. & Novikov, A. Immersion Freezing of a Scots Pine Single Seed in a Water-Saturated Dispersion Medium: Mathematical Modelling. Inventions 5 , 51 (2020). Novikov, A. et al. The effect of low-intensive coherent seed irradiation on germinant growth of Scots pine and sugar beet. J. Sci. 67 , 427–435 (2021). Vovchenko, N. G., Novikov, A. I., Sokolov, S. V. & Tishchenko, E. N. New technology for encapsulating conditioned seeds to increase aerial seeding efficiency. IOP Conf. Ser. Earth Environ. Sci. 595 , 012009 (2020). Barnett, J. P. Relating pine seed coat characteristics to speed of germination, geographic variation, and seedling development. Tree Plant. Notes . 48 , 38–42 (1997). Novikov, A. I., Ivetić, V., Novikova, T. P. & Petrishchev, E. P. Scots pine seedlings growth dynamics data reveals properties for the future proof of seed coat color grading conjecture. Data 4 , 106 (2019). Mandizvo, T. & Odindo, A. O. Seed mineral reserves and vigour of Bambara groundnut (Vigna subterranea L.) landraces differing in seed coat colour. Heliyon 5 , e01635 (2019). Li, W. et al. Distinct Effects of Seed Coat and Flower Colors on Metabolite Contents and Antioxidant Activities in Safflower Seeds. Antioxidants 12 , 961 (2023). Novikova, T. P., Mastrangelo, C. B., Tylek, P., Evdokimova, S. A. & Novikov A. I. How Can the Engineering Parameters of the NIR Grader Affect the Efficiency of Seed Grading? Agriculture 12, 2125 (2022). Novikov, A. I., Ersson, B. T., Malyshev, V. V., Petrishchev, E. P. & Ilunina, A. A. Mechanization of coniferous seeds grading in Russia: a selected literature analysis. IOP Conf. Ser. Earth Environ. Sci. 595 , 012060 (2020). Tylek, P., Demidov, D. N., Lysych, M. N., Petrishchev, E. P. & Maklakova, E. A. The features designed of mechatronic system of adaptive hopper’s feeder: case study for Scots pine seeds morphometry. IOP Conf. Ser. Earth Environ. Sci. 595 , 012054 (2020). Bacherikov, I., Novikov, A. & Petrishchev, E. Discrete Seed Feeder Designing for Mobile Apparatus: Early Results for Pinus sylvestris L. Species Inventions . 6 , 14 (2021). Novikov, A. I., Zolnikov, V. K. & Novikova, T. P. Grading of Scots pine seeds by the seed coat color: how to optimize the engineering parameters of the mobile optoelectronic device. Inventions 6 , 7 (2021). Novikov, A. I., Drapalyuk, M. V., Dornyak, O. R., Zelikov, V. A. & Ivetić, V. The Effect of Motion Time of a Scots Pine Single Seed on Mobile Optoelectronic Grader Efficiency: A Mathematical Patterning. Inventions 4 , 55 (2019). Novikov, A. I. et al. Improving the quality of automated VIS–grading of Scots pine seeds using fuzzy logic algorithm. IOP Conf. Ser. Earth Environ. Sci. 875 , 012032 (2021). Albekov, A. U. et al. Express analyzer of seed quality. RU Patent 2 (2018). 675 056, 14 December 2018. Sokolov, S. V., Kamenskij, V. V., Novikov, A. I. & Ivetić, V. How to increase the analog-to-digital converter speed in optoelectronic systems of the seed quality rapid analyzer. Inventions 4 , 61 (2019). Novikova, T. P. & Novikov, A. I. Economic evaluation of mathematical methods application in the management systems of electronic component base development for forest machines. IOP Conf. Ser. Earth Environ. Sci. 392 , 012035 (2019). Sokolov, S. V. & Novikov, A. I. New optoelectronic systems for express analysis of seeds in forestry production. Eng. J. 9 , 5–13 (2019). Novikov, A. I. & Ersson, B. T. Aerial seeding of forests in Russia: A selected literature analysis. IOP Conf. Ser. Earth Environ. Sci. 226 , 012051 (2019). Sokolov, S. & Novikov, A. I. Development tendency of sowing air operating technology by unmanned aerial vehicles in artificial reforestation. Eng. J. 7 , 190–205 (2017). Novikova, T. P. The choice of a set of operations for forest landscape restoration technology. Inventions 7 , 1 (2022). Novikova, T. Study of a set of technological operations for the preparation of coniferous seed material for reforestation. Eng. J. 11 , 150–160 (2022). Novikov, A., Rabko, S., Novikova, T. & Petrishchev, E. The effect of the individual seed mass of Negorelskaya variety Scots pine (Pinus sylvestris L.) on 30-day germination in 40-cell SideSlit growing containers. Eng. J. 13 , 59–86 (2023). Novikov, A. I. Germination in containers of Scots pine seeds: effect of grading by colour and size. Conifers Boreal Area . XXXVII , 313–319 (2019). Mañas, P., Castro, E. & De Heras, L. Quality of maritime pine (Pinus pinaster Ait.) seedlings using waste materials as nursery growing media. New. For. 37 , 295–311 (2009). Novikov, A. I. & Novikova, T. P. Non-destructive quality control of forest seeds in globalization: problems and prospects of output innovative products. in Globalization and Its Socio-Economic Consequences (ed Kliestik, T.) 1260–1267 (Univ Zilina, Rajecke Teplice, Slovakia, (2018). Moscetti, R. et al. Pine nut species recognition using NIR spectroscopy and image analysis. J. Food Eng. 292 , 110357 (2021). Liu, W., Liu, J., Jiang, J. & Li, Y. Comparison of partial least squares-discriminant analysis, support vector machines and deep neural networks for spectrometric classification of seed vigour in a broad range of tree species. J. Near Infrared Spectrosc. 096703352096375 10.1177/0967033520963759 (2020). Hacisalihoglu, G. & Armstrong, P. Crop Seed Phenomics: Focus on Non-Destructive Functional Trait Phenotyping Methods and Applications. Plants 12 , 1177 (2023). Wang, D., Dowell, F. E. & Lacey, R. E. Predicting the Number of Dominant R Alleles in Single Wheat Kernels Using Visible and Near-Infrared Reflectance Spectra. Cereal Chem. 76 , 6–8 (1999). Novikov, A., Lisitsyn, V., Tigabu, M., Tylek, P. & Chuchupal, S. Detection of Scots pine single seed in optoelectronic system of mobile grader: mathematical modeling. Forests 12 , 240 (2021). Novikov, A. I. Rapid Analysis of Forest Seeds: Biophysical Methods (VSUFT, Voronezh, 2018). Shrestha, S., Deleuran, L. C. & Gislum, R. Separation of viable and non-viable tomato (Solanum lycopersicum L.) seeds using single seed near-infrared spectroscopy. Comput. Electron. Agric. 142 , 348–355 (2017). Tigabu, M. & Odén, P. C. Simultaneous detection of filled, empty and insect-infested seeds of three Larix species with single seed near-infrared transmittance spectroscopy. New. For. 27 , 39–53 (2004). Masilamani, P. et al. Role of Near - Infrared Spectroscopy in Seed Quality Evaluation: A Review. Agric. Rev. 10.18805/ag.r-1960 (2020). Hacisalihoglu, G., Larbi, B. & Settles, A. M. Near-Infrared Reflectance Spectroscopy Predicts Protein, Starch, and Seed Weight in Intact Seeds of Common Bean (Phaseolus vulgaris L). J. Agric. Food Chem. 58 , 702–706 (2010). Silva, M. T. D. et al. Innovative substrates for sugarcane seedling production: Sewage sludges and rice husk ash in a waste-to-product strategy. Ind Crops Prod 157 , (2020). Esteve Agelet, L. & Hurburgh, C. R. Limitations and current applications of Near Infrared Spectroscopy for single seed analysis. Talanta 121 , 288–299 (2014). Nehoshtan, Y., Carmon, E., Yaniv, O., Ayal, S. & Rotem, O. Robust seed germination prediction using deep learning and RGB image data. Sci. Rep. 11 , 22030 (2021). Bernardes, R. C. et al. Deep-Learning Approach for Fusarium Head Blight Detection in Wheat Seeds Using Low-Cost Imaging Technology. Agriculture 12 , 1801 (2022). Przybyło, J. & Jabłoński, M. Using Deep Convolutional Neural Network for oak acorn viability recognition based on color images of their sections. Comput. Electron. Agric. 156 , 490–499 (2019). Dell’Aquila, A. Red-Green-Blue (RGB) colour density as a non-destructive marker in sorting deteriorated lentil (Lens culinaris Medik.) seeds. Seed Sci. Technol. 34 , 609–619 (2006). de Castro, É. B. Classification of Phaseolus lunatus L. using image analysis and machine learning models. Rev. Caatinga . 35 , 772–782 (2022). Zhang, R., Kan, Z., Ma, R., Cao, W. & Li, J. Relationship between color features and germination of delinted cottonseed based on RGB color model. Nongye Gongcheng Xuebao/Transactions Chin. Soc. Agric. Eng. 26 , 172–177 (2010). Silva, Á. M. et al. Densified biochar capsules as an alternative to conventional seedings. J. Environ. Manage. 348 , 119305 (2023). Carvalho, M. E. A., Labate, C. A., Barboza da Silva, C., de Camargo, P. R. & Azevedo, R. A. Seed photorespiration: a perspective review. Plant Growth Regul. 97, 477–484 (2022). da Barboza, C. et al. Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality. Sci. Rep. 11 , 17834 (2021). Bianchini, V. J. et al. Multispectral and X-ray Images for Characterization of Jatropha Curcas L. Seed Quality. Plant Methods 1–14 at (2020). https://doi.org/10.21203/rs.3.rs-28449/v1 Novikova, T. P. Seed – culture’ technological passport: RGB-brightness and RGB-saturation identification of Pinus sylvestris L. var. Negorelskaya individual seeds based on the author’s technique. Eng. J. 14 , 37–60 (2024). Feng, G. et al. Wheat Fusarium Head Blight Automatic Non-Destructive Detection Based on Multi-Scale Imaging: A Technical Perspective. Plants 13 , 1722 (2024). Xu, Y. et al. AIseed Simulation: A seed simulation sorting software for rapidly determining seed processing procedures and parameters. Comput. Electron. Agric. 221 , 108971 (2024). Parfenova, E. I., Kuzmina, N. A., Kuzmin, S. R. & Tchebakova, N. M. Climate Warming Impacts on Distributions of Scots Pine (Pinus sylvestris L.) Seed Zones and Seed Mass across Russia in the 21st Century. Forests 12 , 1097 (2021). Beaton, J. et al. Phenotypic trait variation in a long-term multisite common garden experiment of Scots pine in Scotland. Sci. Data . 9 , 671 (2022). Novikova, T. P., Novikov, A. I. & Petrishchev, E. P. FLR-Library reference information system for adaptive forest restoration: cluster analysis of descriptors. Eng. J. 13 , 164–179 (2023). Busin, L., Vandenbroucke, N. & Macaire, L. Color Spaces and Image Segmentation. Adv. Imaging Electron. Phys. 151 , 65–168 (2009). (Elsevier Masson SAS. Abeytilakarathna, P. D., Fonseka, R. M., Eeswara, J. P. & Herath, H. M. D. K. Refractive index and spectral reflection in three leaf categories of strawberry (Fragaria x ananassa Duch). Trop. Agric. Res. 25 , 261 (2015). Mamaev, S. A. Forms of Intraspecific Variability of Woody Plants (on the Example of Pinaceae Family in the Urals) (Science Publ., 1973). Novikov, A. I. Technology of Scots Pine Seeds Grading on a Quantitative Attribute: Some Results of Approbation. Izv. Sankt-Peterburgskoj Lesoteh Akad. 68–89. 10.21266/2079-4304.2019.227.68-87 (2019). Additional Declarations No competing interests reported. Supplementary Files NovikovaEtAl228ManuscriptRGB07ENSciReportssupplementary.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6639938","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":486574089,"identity":"f06fc212-4d96-4cf8-8c33-03e9687e80e5","order_by":0,"name":"Tatyana P. Novikova","email":"","orcid":"","institution":"Voronezh State University of Forestry and Technologies named after G.F. Morozov","correspondingAuthor":false,"prefix":"","firstName":"Tatyana","middleName":"P.","lastName":"Novikova","suffix":""},{"id":486574090,"identity":"891feac8-8ac5-4eaa-82c8-0465647e38ba","order_by":1,"name":"Paweł Tylek","email":"","orcid":"","institution":"University of Agriculture in Krakow","correspondingAuthor":false,"prefix":"","firstName":"Paweł","middleName":"","lastName":"Tylek","suffix":""},{"id":486574091,"identity":"53ebd489-e648-4a83-bcd6-fa39ed85c833","order_by":2,"name":"Arthur I. Novikov","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYLACHgYbIMnYeIBYDYwNPAxpYJokLYfBLOK0mEskP3/wpuK83dr2w0BbamyiCWqxnJFm2DjnzO3kbWcSgVqOpeU2ENJicOaAYTNv2+1kswNALYwNh4nRcvwjUMu5ZLPzD4nVcrwHZMsBO7MbRNtyvKdw5pwzyQlmN4C2JBDll8PsGz68qbCzNzuf/vDBhxobwlpgIBGsMoFY5SBgT4riUTAKRsEoGGEAAEtJSu5EQycnAAAAAElFTkSuQmCC","orcid":"","institution":"Agrophysical Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Arthur","middleName":"I.","lastName":"Novikov","suffix":""}],"badges":[],"createdAt":"2025-05-11 13:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6639938/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6639938/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87363222,"identity":"c6d2a099-958c-4216-b733-e66f2ff842b4","added_by":"auto","created_at":"2025-07-23 06:03:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":62532,"visible":true,"origin":"","legend":"\u003cp\u003eMass of an individual seed of \u003cem\u003ePinus sylvestris\u003c/em\u003e L. cv. Negorelskaya: (a) boxplots of values in groups of nonzero (1) and zero (0) germination in containers on the 30th day after sowing (** the null hypothesis of group equality is rejected by the nonparametric Kolmogorov‒Smirnov criterion D = 0.1226 at p = 0.0045); (b) QQplot; (c) distribution of values in the nonzero germination group; (d) distribution of values in the zero-germination group.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6639938/v1/10b6f698db94f63cecd17dc1.png"},{"id":87363223,"identity":"b0745007-12bc-4245-95fa-9f44e1eff85d","added_by":"auto","created_at":"2025-07-23 06:03:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":62473,"visible":true,"origin":"","legend":"\u003cp\u003eThe R-parameter to pixel brightness in the image of an individual seed of \u003cem\u003ePinus sylvestris\u003c/em\u003e L. cv. Negorelskaya: (a) boxplots of values in groups of nonzero (1) and zero (0) germination in containers on the 30th day after sowing (**** null-the hypothesis of group equality is rejected by the nonparametric Kolmogorov‒Smirnov criterion D = 0.3540 at p \u0026lt; 0.0001; (b) QQ-Plot; (c) distribution of values in the nonzero germination group (1-group); (d) distribution of values in the zero germination group (0-group).\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6639938/v1/0f7884ff0d9d19e839c70d86.png"},{"id":87362353,"identity":"ad5b76c2-f6db-4c6d-9549-652dbe390aed","added_by":"auto","created_at":"2025-07-23 05:55:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":58146,"visible":true,"origin":"","legend":"\u003cp\u003eThe G-parameter is the pixel brightness in images of individual seeds of \u003cem\u003ePinus sylvestris\u003c/em\u003e L. cv. Negorelskaya: (a) boxplots of values in groups of nonzero (1) and zero (0) germination in containers on the 30th day after sowing (**** the null hypothesis of group equality is rejected by the nonparametric Kolmogorov‒Smirnov criterion D = 0.3372 at p \u0026lt; 0.0001); (b) QQ plot; (c) distribution of values in the nonzero germination group; (d) distribution of values in the zero germination group.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6639938/v1/d0e7390e9bffdc6918bd6230.png"},{"id":87362358,"identity":"2dae8c0f-f24c-437b-95d7-fba1fe45fa38","added_by":"auto","created_at":"2025-07-23 05:55:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":56730,"visible":true,"origin":"","legend":"\u003cp\u003eB-parameter of pixel brightness in images of individual \u003cem\u003ePinus sylvestris\u003c/em\u003e L. c. negorelskaya seeds: (a) boxplots of values in groups of nonzero (1) and zero (0) germination in containers on the 30th day after sowing (**** the null hypothesis of group equality is rejected by the nonparametric Kolmogorov‒Smirnov criterion D = 0.3017 at p \u0026lt; 0.0001); (b) QQ-plot; (c) distribution of values in the nonzero germination group; (d) distribution of values in the zero germination group.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6639938/v1/c14fc62e1f5bf4e89355c70c.png"},{"id":87362355,"identity":"f158962b-d63d-45ea-ab3b-62b4ee89726b","added_by":"auto","created_at":"2025-07-23 05:55:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2811,"visible":true,"origin":"","legend":"\u003cp\u003ePalette of median RGB values of pixel brightness in images of individual \u003cem\u003ePinus sylvestris\u003c/em\u003e L. cv. Negorelskaya seeds: (a) for 1-group (R80 G72 B67); for 0-group (R110 G91 B77).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6639938/v1/e92c13377bbaf7cec69ef8c8.png"},{"id":87549880,"identity":"0de72da1-0427-4594-ab9e-80981b28e90f","added_by":"auto","created_at":"2025-07-25 06:02:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1006456,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6639938/v1/b67b2194-f500-4e88-a29d-f585bdffb4fe.pdf"},{"id":87363226,"identity":"7bdbf3de-5bbf-48eb-851d-a35647cd7d64","added_by":"auto","created_at":"2025-07-23 06:03:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":647032,"visible":true,"origin":"","legend":"","description":"","filename":"NovikovaEtAl228ManuscriptRGB07ENSciReportssupplementary.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6639938/v1/95ceaec216f1203245be1ae4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"How Germination Changes During Individual Seed RGB-Space Differentiation: The Case of Pinus sylvestris L. сv. Negorelskaya","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe success of forest plantations, like protective forest strips \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e or field shelterbelts \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, or seed orchards \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, depends on how well the trees used can handle biotic (living) and abiotic (non-living) environmental factors. In turn, these characteristics are determined at the genetic level and determine the quality \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e of Forest Reproductive Material (FRM). As rightly noted, Clara Tattoni et al. \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e from the DAGRI: \u003cem\u003e“Accurate estimates of forest seed production are central for a wide range of ecological studies\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e”. The productivity of forest crops can be improved through breeding tests.\u003c/p\u003e\u003cp\u003eThe main purpose of the entire funded study was to track, evaluate, and analyze the individual reproduction cycles of 1,200 plants of \u003cem\u003ePinus sylvestris\u003c/em\u003e L. cv. Negorelskaya. We have created a «seed-culture» technological passport, starting from the datasets of the spectral and germination properties of each seed.\u003c/p\u003e\u003cp\u003eA \u003cem\u003eP. sylvestris\u003c/em\u003e cv. Negorelskaya is a breeding cultivar \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e characterized by intensive growth and early abundant seed production (up to 15%). All other things being equal, it's going to come down to the seeds quality \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. The better the seeds \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, the better the plants. And the seedlings quality \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e is closely linked to its seeds physical properties: seeds weights (or mass), seeds size \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, seeds color – seed visible features \u003csup\u003e\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e–\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, seed infrared features \u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e–\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, seed immersion features \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, seed irradiation features \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, seed conditioned features \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. All these studies were conducted on group seed samples. For example, weight was determined for 1,000 seeds, color characteristics were evaluated for 100 seeds, and seed germination was calculated for another 100 seeds.\u003c/p\u003e\u003cp\u003eThe individual mass of each seed and its individual visual spectral characteristics (seed coat color \u003csup\u003e\u003cspan additionalcitationids=\"CR24 CR25\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e–\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, seed epidermis color, or pericarp color \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e) are important indicators of seeds quality. The first result, according to Professor John A. Stanturf, is quite well known. The second result is interesting and may have some significance for quick seed sorting \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Seeds quality criteria \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e are also taken into account when designing seeds hoppers \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, feeders \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, graders \u003csup\u003e\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e–\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, and analyzers \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e, their electronic \u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e and optoelectronic \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e components. Also, the success of a bunch of different seeding techniques, like aerial seeding \u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e,\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e on hard-to-cultivate sites, depends on how good the seed is.\u003c/p\u003e\u003cp\u003eRational assessment of the effectiveness of reforestation technology \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e by \u003cem\u003eP. sylvestris\u003c/em\u003e L. cv. Negorelskaya when moving seeds to the experimental site according to the climatic gradient, \"\u003cem\u003edependence of accumulated precipitation on accumulated degree days\u003c/em\u003e\" \u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, used in this study, is based on the hypothesis of the influence of spectrometric seed parameters on germination rates on day 30 \u003csup\u003e43,44\u003c/sup\u003e in containers of an automatized forest nursery.\u003c/p\u003e\u003cp\u003eThe accuracy of nondestructive \u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e detection of forest seeds via electromagnetic waves from different regions increases with the individual evaluation of each single seed \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e–\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, which is affected by the focal distance \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e from the sensor to the point of reflection of the electromagnetic beam from the surface of the seed coat \u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. Moreover, depending on the type and nature of exposure to electromagnetic radiation, the biophysical \u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e parameters of the seed may be correlated with different properties and indicators of a single seed: biochemical content of lipids \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, starch \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, protein \u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e, trace elements \u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, etc., as well as physiological germination.\u003c/p\u003e\u003cp\u003eThe accuracy of detection also depends on the quality of the seed (size, shape, heterogeneity) and the characteristics of the detector. Esteve Agelet et al. (2014) conclude from a systematic search for 168 references for single seed identification that \"\u003cem\u003ealthough no measurement mode (reflectance, transmittance) have led to the best reported calibrations, when dealing with heterogeneous seeds reflection is the best working mode\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e\". In most studies of forest single seeds, expensive devices are used to create and detect electromagnetic radiation (VIS, NIR) reflected from cameras with prolonged exposure. Some researchers use rather expensive USB cameras with autofocus \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e, which are independent of the exhibition. However, the use of an inexpensive scanner \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e or a smartphone may be sufficient for the predictive evaluation of seedlots.\u003c/p\u003e\u003cp\u003eTo quantify the color of the seed coat in the VIS region of visible light wavelengths, Red‒Green‒Blue (RGB-space) is often used, for example, in Yuval Nehoshtan et al. \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e, Jaromir Przybyło and Mirosław Jabłoński \u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, which consists of estimating the medium color density (from 0–250) of an RGB image, as in Antonio Dell'Aquila \u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e, or estimating the color density of each of the three color channels (R, G, or B), as in Érika Beatriz de Lima Castro et al. \u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e, and Ruoyu Zhang et al. \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAn express semantic analysis of keywords in the LENS system for seed research \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan additionalcitationids=\"CR65 CR66\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e–\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e on the basis of their images, organized according to an ORCID query by Clissia B. da Silva, returns the \"\u003cem\u003egermination\u003c/em\u003e\" descriptor in a third of the cases. This finding is convincing enough to reveal a connection between the spectral characteristics of the seed epidermis and germination.\u003c/p\u003e\u003cp\u003eThe purpose of this paper was to evaluate the degree of relationship in the RGB space between the color of the seed coat of \u003cem\u003eP. sylvestris\u003c/em\u003e L. cv. Negoretskaya and the seed quality determined by germination in the cells of an nursery container.\u003c/p\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"2. Results","content":"\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eIn this section, we demonstrate the results of 30-day germination of individual seeds (N\u0026thinsp;=\u0026thinsp;1200) of \u003cem\u003ePinus sylvestris\u003c/em\u003e L. сv. Negorelskaya, depending on the individual RGB parameters of the color of the seed coat. All the considered RGB parameters (measured and normalized) differed significantly between the zero (1-group) and zero (0-group) germination groups (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eVariability of the measured and specific RGB color parameters of the seed coats of individual \u003cem\u003ePinus sylvestris\u003c/em\u003e L. cv. Negorelskaya seeds in groups with nonzero (1-group) and zero (0-group) container-based germination for 30 days after seeding.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePatameter\u0026rsquo;s Title\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026laquo;1\u0026raquo; - germination group\u003c/p\u003e\n \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;942);\u003c/p\u003e\n \u003cp\u003emean | \u0026plusmn;SD | CV%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026laquo;0\u0026raquo; - germination group (N\u0026thinsp;=\u0026thinsp;258);\u003c/p\u003e\n \u003cp\u003emean | \u0026plusmn;SD | CV%\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u0026laquo;1\u0026raquo; vs. \u0026laquo;0\u0026raquo;\u003c/p\u003e\n \u003cp\u003eP value mean\u0026rsquo;s different\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKolmogorov‒Smirnov D\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeed individual mass (m), g\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006098 | 0.001672 | 27.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005673 | 0.001961 | 34.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0045\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1226\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePixel brightness of the R-channel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.42 | 23.81 | 27.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e109.10 | 31.33 | 28.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3540\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePixel brightness of the G-channel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75.96 | 16.68 | 21.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.60 | 23.30 | 25.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3372\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePixel brightness of the B-channel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69.51 | 12.31 | 17.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.28 | 18.02 | 22.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3017\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecific pixel brightness of the R-channel r\u0026thinsp;=\u0026thinsp;R/(R\u0026thinsp;+\u0026thinsp;G\u0026thinsp;+\u0026thinsp;B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3690 | 0.0247 | 6.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3820 | 0.0239 | 6.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecific pixel brightness of the G-channel g\u0026thinsp;=\u0026thinsp;G/(R\u0026thinsp;+\u0026thinsp;G\u0026thinsp;+\u0026thinsp;B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3278 | 0.0062 | 1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3275 | 0.0055 | 1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0429\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpecific pixel brightness of the B-channel b\u0026thinsp;=\u0026thinsp;B/(R\u0026thinsp;+\u0026thinsp;G\u0026thinsp;+\u0026thinsp;B)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3031 | 0.0225 | 7.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2905 | 0.0218 | 7.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe mass parameter of an individual seed in both germination groups has a distribution at the tails that deviates slightly from the Gauss curve (Fig. 1, b); therefore, to evaluate the null hypothesis that there are no differences between the distributions of values in the groups, we apply the nonparametric Kolmogorov‒Smirnov D criterion. In both groups of seeds (with zero and nonzero germination), there is a moderate tendency toward low values of the individual weight parameter, since the medians (Me [Q1; Q3]) of the parameter values for the group are zero (5.5 mg [4.5; 7]) and nonzero (6.0 mg [5; 7]) germination is focused slightly to the left of the mean value (Fig. 1, a) and determines a slight left-sided asymmetry. The interquartile range of the IQR (Q3\u0026ndash;Q1) parameter characterizes the nonzero germination group (2.0 versus 2.5 mg) as more robust to outliers. Moreover, the groups are characterized by an increased level (see the CV parameter in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) of individual weight factor variability, and 27.9% of the seeds in group 1 (Fig. 1, c) and 22.1% of the seeds in group 0 (Fig. 1, d) have a mass of 6 mg. Nevertheless, it is statistically shown (Fig. 1) that the lower values of the individual seed weight from group 0 than those from group 1 are not accidental at a confidence level of p\u0026thinsp;=\u0026thinsp;0.0045.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe pixel brightness parameter R in the image of an individual seed in both germination groups has a distribution at the tails that deviates slightly from the Gauss curve (Fig. 2, b); therefore, to evaluate the null hypothesis of the absence of differences between the distribution of values in the groups, we apply the nonparametric Kolmogorov‒Smirnov criterion D. In the group of seeds with zero germination, there is a strong approximation of the median to the average value, namely, 109.5 [80.0; 134.3] to 109.1, but still on the right, which implies a minimal shift toward the prevalence of high values of R. In the group of seeds with nonzero germination, there is a moderate tendency of the R-parameter to low values, since the median of 80 [69; 99] parameter values is positioned on the left, in the middle between the mean value (Fig. 2, a) and Q1, which characterizes a moderate left‒sided asymmetry. The IQRs of the R parameter values characterize the nonzero germination group (30.0 vs. 54.3) as more resistant to outliers. At the same time, both groups are characterized by an increased level (see the CV parameter in the R-row in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) of variability in the pixel brightness factor of the seed epidermis image in R-space; 24% of the seeds in the 1-group (Fig. 2, c) have an R-brightness of 70,12% of the seeds in the 0-group (Fig. 2, d) \u0026ndash; R-brightness of 130. Nevertheless, it was statistically shown (Fig. 2) that the greater R-brightness of the epidermis of the 0-group seeds was not accidental (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe distributions of the values of the G-brightness parameter of pixels in the image of an individual seed in the germination 1-group are moderate, and in the 0-group, they deviate significantly from the Gauss curve (Fig. 3, b); therefore, the nonparametric Kolmogorov‒Smirnov criterion D was used to evaluate the null hypothesis about the absence of differences between the distributions of values in the groups. In the 0-group of seeds, as well as in the R-brightness (see Fig. 2, a), there is a strong approximation from the median 91 [71.75; 111.3] to the average value of 92.6, but on the left, which implies a minimal shift toward the prevalence of lower values of G-brightness (Fig. 3, a). In the 1-group of seeds, there is a moderate tendency of the G-parameter to low values (as in the R-brightness), since the median 72 [65; 83] is focused on the left, almost in the middle between the mean value of 75.95 (Fig. 3, a) and Q1, which characterizes a moderate left-sided asymmetry. The interquartile range (IQR) of the distribution of G-parameter values characterizes the 1-group (18.0 vs. 39.55 for the 0-group) as more robust to outliers. Moreover, Group 1 is characterized by an average (21.96%), and Group 0 is characterized by an increased level (see the CV parameter in the G row in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) of variability in the pixel brightness factor of the image of the epidermis of the seed in G space. Notably, 17.8% of the seeds in the 1-group (Fig. 3, c) presented a G-brightness of 70, and 10.9% of the seeds in the 0-group (Fig. 3, d) presented a G-brightness of 70. Nevertheless, it is statistically shown (Fig. 3) that higher values of G-brightness in the epidermis of seeds from the 0-group are not noticeable at a confidence level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe distributions of the pixel brightness parameter B in the image of an individual seed in germination group 1 are moderate, and in group 0, they deviate significantly from the Gauss curve (Fig. 4, b); therefore, the nonparametric Kolmogorov‒Smirnov D criterion was used to evaluate the null hypothesis that there are no differences between the distributions of values in the groups. In the 0-group of seeds, there is a moderate location of the median of 76.5 [66; 97] to the left of the mean value of 81.3, which suggests a moderate tendency toward the prevalence of lower values of B-brightness (Fig. 4, a). In the mean group 1, there is a moderate tendency of the B parameter to have low values (as in the R and G luminosities), since the median 67 [61; 75] is focused on the left, almost in the middle between the mean value of 69.5 (Fig. 4, a) and Q1, which characterizes moderate left‒sided asymmetry. The interquartile range (IQR) of the distribution of the values of the B parameter characterizes the 1-group (14 versus 31 in the 0-group) as more robust to outliers. In this case, group 1 and group 0 are characterized by the average (17.76 and 22.17%, respectively) level (see the CV parameter in the B-row in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) variability of the pixel brightness factor of the image of the epidermis of the seed in the B-space. Notably, 22.6% of the seeds in group 1 (Fig. 4, c) have a B-brightness of 65, and 17.1% of the seeds in group 0 (Fig. 4, d) have a B-brightness of 65. Nevertheless, it is statistically shown (Fig. 4) that the higher values of B-brightness in the epidermis of the seeds from the 0-group are not random at a confidence level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThus, for the 1 200 breeding seeds included in this study, it is statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) that brighter seeds reflecting most of the light rays from the epidermis will have lower container germination. Visually, this can be represented as a palette on a black background of the median RGB brightness values for the two germination groups (Fig. 5).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Discussion","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eWe discuss the results of applying physical pixel brightness indices and their derivatives, normalized reflection indices, for potential evaluation of seed productivity: how they can be interpreted from the point of view of previous studies and working hypotheses. Note that the results and their significance should be interpreted within the framework of the experimental conditions.\u003c/p\u003e\u003cp\u003e\u003cb\u003eImage acquisition method as an interpretation criterion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFirst, we note that obtaining an image of the epidermis of seeds should strive for the optimum, at which the cost of using devices would not exceed the productive efficiency of plants. In other words, the lowest resolution of the seed image and the minimum time of its exposure and processing, at which it is possible to detect statistically significant differences between the dichotomous groups of seed germination, would be preferable when choosing a phenotyping method in the future.\u003c/p\u003e\u003cp\u003eThe second fundamentally important factor is the R\u0026amp;D focus: one set of instruments and instruments is needed for scientific purposes, and another is needed for production conditions.\u003c/p\u003e\u003cp\u003eThe third factor determining the choice of statistical apparatus and the accuracy of the results is the level of certification of the sample: group \u0026ndash; when a sample of seeds from 10 to 1000 or more pieces is selected as a single sample; individual \u0026ndash; when a group of individual seeds is used as a single sample, each of which has a technological passport \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e, including morphometric and biophysical parameters and replenished as the seed germinates and the culture obtained from it grows early.\u003c/p\u003e\u003cp\u003eIn this study, we used a budget flatbed scanner available in almost every laboratory with a minimum image resolution of 300 dpi and obtained significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in the distribution of RGB spectral indices for germinating and nongerminating seeds of \u003cem\u003ePinus sylvestris\u003c/em\u003e L. cv. Negorelskaya, which is the continuation of research toward reducing the resolution of the scanner (for example, up to 150 dpi to search for the lowest possible resolution for identification).\u003c/p\u003e\u003cp\u003eAs in Yuval Nehoshtan et al. \u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e, a color bodyless machine vision camera with an IDS UI-3884LE-C-HQ-AF liquid lens with a resolution of 6.41 megapixels (3088 \u0026times; 2076 pixels), on the one hand, significantly increases the cost of seed detection, and on the other hand, it allows the assembly of an engineering and technically automated system with a controlled camera. For example, Guoqing Feng et al. reported that the accuracy of the dichotomous classification of wheat grains by health (healthy/infected with a fusarium) based on VIS images in the RGB space can reach 97% \u003csup\u003e69\u003c/sup\u003e, and Bernardes et al. \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e reported an accuracy of 99% when the Epson Perfection V800 flatbed scanner was used.\u003c/p\u003e\u003cp\u003e\u003cb\u003eRGB index as an interpretation criterion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe following was accepted as the null hypothesis: the RGB index values in two dichotomous germination groups (0-germination and 1-germination) had a sufficiently high probability of being distributed equally. However, for all the RGB indices in this study, it is statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) that the high values of the indices in the 0-group compared with those in the 1-group are not accidental. This means that the biophysical criterion\u0026mdash;the color of the seed epidermis\u0026mdash;has great potential for predicting the physiological parameters of germination (that is, the manifestation of the phenotype).\u003c/p\u003e\u003cp\u003eR. Zhang et al. \u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e reported a regression relationship R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.7148 (p\u0026thinsp;=\u0026thinsp;0.01) for cotton seeds between the normalized parameters of color characteristics R/(R\u0026thinsp;+\u0026thinsp;G\u0026thinsp;+\u0026thinsp;B), G/(R\u0026thinsp;+\u0026thinsp;G\u0026thinsp;+\u0026thinsp;B), (R-G-B)/(R\u0026thinsp;+\u0026thinsp;G), and germination. Xu Yan et al. \u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e developed software to simulate the sorting of seeds of five species in real time via the color ratios in RGB-space, which demonstrated a fairly high correlation when the purity of a seed batch was predicted.\u003c/p\u003e\u003cp\u003eIn the future, projects and collaborations based on these data descriptors are possible, for example, with a group of scientists led by C.B. Mastrangelo \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, who will be aimed at developing informatization of forest management systems and will combine the ambitious goal of tracing and ensuring the ability to effectively manage the process of restoring forest landscapes \u0026ldquo;from seeds to forest crops\u0026rdquo; on the example of Scots pine (\u003cem\u003eP. sylvestris\u003c/em\u003e L.). In the future, we plan to expand research \u0026ldquo;from seeds to forest crops\u0026rdquo; to other types of woody plants.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"4. Materials and methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Seed collection\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThree samples (n\u0026thinsp;=\u0026thinsp;400) of dewinged seeds were selected by quartering \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e from a seedlot of Scots pine (\u003cem\u003ePinus sylvestris\u003c/em\u003e L. сv. Negorelskaya) collected in 2023 from a location (53.577939, 27.056128, 180 m asl).\u003c/p\u003e\u003cp\u003eThe authors confidently state that \u003cem\u003ePinus sylvestris\u003c/em\u003e L. сv. Negorelskaya is a cultivar. Detailed information on cultivar breeding was provided by Rabko et al. \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The information about this is confirmed by the certificate for variety No. 0003707 and the breeder's certificate No. 0005065, with priority dated 28.03.2008, issued to the author S.U. Rabko and colleagues by the Ministry of Agriculture and Food of the Republic of Belarus.\u003c/p\u003e\u003cp\u003eThe issue of italicizing the epithet Negorelskaya is quite debatable. On the one hand, the International Code of Nomenclature of Cultivated Plants (ICNCP) recommends against italicizing epithets below the species level. On the other hand, there are studies that provide fairly acceptable arguments for the use of italics throughout the taxonomic description (for example, Marco Thines et al. (2020) https://imafungus.biomedcentral.com/articles/\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s43008-020-00048-6#Sec12\u003c/span\u003e\u003cspan address=\"10.1186/s43008-020-00048-6#Sec12\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Therefore, the epithet Negorelskaya is written without italics.\u003c/p\u003e\u003cp\u003eCurrently, seeds of Scots pine (\u003cem\u003eP. sylvestris\u003c/em\u003e L. сv. Negorelskaya) for germination experiments in the gradation function \u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e of the accumulated annual precipitation (mm) depending on the accumulated degree days \u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e of the region. The current experiment is no exception: 1200 varietal seeds were removed from the collection site (1731 degree days, 722 mm) to the experimental site (2326 degree days; 786 mm).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Datasets creation\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe library of forest reproductive material (FRM-Library) \u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e, which is based on Pravdin's conjecture \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, is expanding and being filled with data on individual parameters and indicators of each single seed of \u003cem\u003eP. sylvestris\u003c/em\u003e L. сv. Negorelskaya.\u003c/p\u003e\u003cp\u003eDataset blocks used in this study are presented in detail additional PDF file (Figure A1, Appendix A) and include:\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eSeed morphometric block (from Dataset 1), included seed individual mass;\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSeed VIS-spectrometric block (from Dataset 2);\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eSeed germination block (from Dataset 3.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Seed image processing\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eScanning, according to the proposed method of the author T.P. Novikova \u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e (Patent application RU 2024137297, 2024-12-12), was performed for 40 seeds, placing them on flatbed scanner glass with a white background \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e in the order of future sowing in containers.\u003c/p\u003e\u003cp\u003eWe preconfigured the field size of the 40-seed scan by clicking the [Preview] button in the scan window of the scanner interface (Brother DCP). The scan paper size was cut to 280*145 mm. For the subsequent study of spectrometric properties and to ensure a sufficient level of subsequent segmentation of the image of the dorsal and ventral projections of the seeds of Scots pine (\u003cem\u003eP. sylvestris\u003c/em\u003e L. сv. Negorelskaya) with a square of 124 \u0026times; 124 pixels, for example, as in Rodrigo K. Bernardes and coauthors \u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, a sufficient level of randomization, as well as minimization of the noise of the CCD scanner matrix, provided for the location of the seeds of Scots pine (\u003cem\u003eP. sylvestris\u003c/em\u003e L. сv. Negorelskaya) at a distance of at least 20 mm from the edge of the tablet in the order corresponding to the order of subsequent sowing of seeds in side-ingot containers.\u003c/p\u003e\u003cp\u003eThe scanning resolution was set to 300 dpi, the scanning mode was color, and the brightness was set to the default value. Moreover, the paper size in the pixels was set to 1718*3309 pixels. Next, the [Scan] button was pressed, and the scan timing was determined via a smart stopwatch background, the value of which was entered into the Excel table. For the period of sample scanning, the time from the appearance of the \"Data Transfer\" window to the appearance of a thumbnail of the scanned seed image in the left menu of the ABBYY Fine Reader program was assumed. The resulting seed scan was saved in uncompressed TIFF format with a file name of the form dI(1\u0026ndash;40)@300\u0026thinsp;=\u0026thinsp;Scan, where d(v) is the conditional dorsal (ventral) orientation of the seed relative to the scanner glass; I (II, III) is the number of random samples of seeds from the seedlot; 1\u0026ndash;40 is the unique seed cipher in the current study; @300 (600,1200) is the scanning resolution, with dots per inch; and =\u0026thinsp;Scan is the color of the reflective substrate of the scanner or colored paper.\u003c/p\u003e\u003cp\u003eThe resulting file (image) has the following numbering of each individual seed. After a sample of 40 seeds corresponding to the future location in side-ingot containers was scanned, the resolution and size of the paper were changed to 600 dots per inch and 3436*6619 pixels, respectively. Moreover, the timing was determined for a resolution of 600 dpi, and the data were entered into the corresponding cell of the Excel table. After the scan was saved, the resolution and paper size were changed to 1200 dpi and 6873*13238 pixels, respectively.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e4.4. Seed germination processing\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe seeds (three samples of four hundred seeds each) were sown manually on June 23, 2023, into each of the 40 cells with a volume of 120 cm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e in HIKO V-120 SideSlit containers (size 352*216*110 mm, 526 seedlings per square meter; BCC AB, Sweden). Each container was prefilled with an acid reaction peat substrate, and the seed was placed in the center of the cell at a depth of 0.5\u0026ndash;1 cm. The location of the seeds for subsequent identification was carried out in accordance with Figure A.7, \u003cem\u003ea (\u003c/em\u003esupp;ementary\u003cem\u003e)\u003c/em\u003e indicating the initial reference cell from the outside with a special marker as in Figure A.7, \u003cem\u003eb (\u003c/em\u003esupp;ementary\u003cem\u003e)\u003c/em\u003e. After sowing 40 seeds, each container was filled with mulch in the form of perlite and placed on a pallet for transportation to the greenhouse. Each sample of 400 seeds was placed in 10 containers. Thirty \u003csup\u003e42 43\u003c/sup\u003e days after seeding, the individual germination of each seed was calculated (0 \u0026ndash; not germinated; 1 \u0026ndash; germinated).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e4.5. Data analysis\u003c/h2\u003e\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe pixel intensities of red, green and blue spectral visible channels (reflected at 700.0, 546.1 and 435.8 nm wavelengths, respectively \u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e) of the segmented individual seed image were analyzed via ImageJ ver. 1.46r open source software, such as P.D. Abeytilakarathna et al. \u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFor each seed in the zero and nonzero germination groups, the following RGB ratios were calculated via the Prism program, version 8.4.3:\u003c/p\u003e\u003cp\u003eThe distribution of indices and normalized RGB indices of segmented images of individual seeds in groups with nonzero and zero germination was visualized via the \"box and whiskers\" diagram in Prism, version 8.4.3. Moreover, the box size represents the interquartile range (IQR), with whiskers indicating the 10th‒90th percentiles. The index distribution was visualized via a frequency diagram, and the degree of normality of the distribution was visualized via a QQ plot.\u003c/p\u003e\u003cp\u003eThe level of variability of the individual seed weight parameters and RGB indicators of the seed coat was estimated on the basis of the values of the CV variation coefficient \u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e,\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e: very low (less than 7%); low (7\u0026ndash;15%); medium (16\u0026ndash;25%); increased (26\u0026ndash;35%); high (36\u0026ndash;50%); and very high (more than 50%).\u003c/p\u003e\u003cp\u003eTo assess the significance of the differences between the distributions of the RGB indices in the zero and nonzero germination groups, the nonparametric Kolmogorov‒Smirnov test D at α\u0026thinsp;=\u0026thinsp;0.05 was used.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThe lower individual seed weight in the 0-group than in the 1-group was not accidental (p\u0026thinsp;=\u0026thinsp;0.0045). Additionally, in the 0-group, the median values of R, G, and B pixel brightness of individual seeds are not accidental (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) compared with those of the 1-group. Thus, for the 1,200 breeding seeds involved in this study, it is statistically significant to state that they are brighter (reflecting most of the light rays from the epidermis) and that the seeds will have a smaller container-grown germination.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp dir=\"LTR\"\u003e\u003cstrong\u003eSupplementary Information:\u0026nbsp;\u003c/strong\u003eThe online version contains supplementary material available at https://doi.org/10.1038/s...\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Conceptualization, A.I.N. and T.P.N.; methodology, A.I.N. and P.T.; software, T.P.N.; validation, A.I.N. and T.P.N.; formal analysis, T.P.N., P.T., A.I.N.; investigation, T.P.N., P.T., and A.I.N.; resources, T.P.N., A.I.N.; data curation, T.P.N., A.I.N.; writing\u0026mdash;original draft preparation, T.P.N., P.T., and A.I.N.; writing\u0026mdash;review and editing, T.P.N., P.T., and A.I.N.; visualization, T.P.N.; supervision, A.I.N.; project administration, T.P.N.; funding acquisition, A.I.N. All\u0026nbsp;the\u0026nbsp;authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research was funded by the Russian Science Foundation (RSF), grant number 23-26-00228, https://rscf.ru/project/23-26-00228/.\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e The authors acknowledge the Chair of Forest Plantations and Soil Science of Belarusian State Technological University (BSTU), for the opportunity to conduct research.\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003eThe authors would also like to acknowledge the reviewers and the editorial board of the \u003cem\u003eScientific Reports\u0026nbsp;\u003c/em\u003ejournal for their valuable comments and recommendations, which have helped increase the reader\u0026apos;s interest in the paper.\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003eData\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eavailability statement\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e The seeds used in this study were collected in 2023 a forest seed orchard (Latitude 53.577939; Longitude 27.056128, Altitude 180 m a.s.l.; Negorelsky Experimental Forestry Center of Belarusian State Technological University, Minsk region, Belarus Republic).\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003eThe original morphometric\u0026nbsp;data\u0026mdash;Dataset 1\u0026mdash;of\u0026nbsp;\u003cem\u003ePinus sylvestris\u003c/em\u003e L. сv.\u0026nbsp;The\u0026nbsp;individual\u0026nbsp;Negorelskaya\u0026nbsp;seeds (N = 1200) presented in the study are openly available in Mendeley Data at DOI\u003cstrong\u003e:\u003c/strong\u003e https://doi.org/10.17632/8g258nbgmf.1. The original VIS image data of \u003cem\u003ePinus sylvestris\u003c/em\u003e L. сv.\u0026nbsp;The\u0026nbsp;individual\u0026nbsp;Negorelskaya\u0026nbsp;seeds (N = 1200) presented in the study are openly available in Mendeley Data at DOI\u003cstrong\u003e:\u003c/strong\u003e https://doi.org/10.17632/dt78jhyw2j.2. The original germination data\u0026mdash;Dataset 3\u0026mdash;of \u003cem\u003ePinus sylvestris\u003c/em\u003e L.\u0026nbsp;cv. The\u0026nbsp;individual\u0026nbsp;Negorelskaya\u0026nbsp;seeds (N = 1200) presented in the study are openly available in Mendeley Data at DOI\u003cstrong\u003e:\u003c/strong\u003e https://doi.org/10.17632/hrs3fgc8tt.1.\u003c/p\u003e\n\u003cp dir=\"LTR\"\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNovikov, A., Ivetic, V., Nikulin, S., Demidov, D. \u0026amp; Petrishchev, E. 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Sankt-Peterburgskoj Lesoteh Akad.\u003c/em\u003e 68\u0026ndash;89. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21266/2079-4304.2019.227.68-87\u003c/span\u003e\u003cspan address=\"10.21266/2079-4304.2019.227.68-87\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2019).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Pinus sylvestris L. сv. Negorelskaya, «seed–culture» technological passport, individual seed coat color, RGB, container-grown germination, agroforest landscape restoration","lastPublishedDoi":"10.21203/rs.3.rs-6639938/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6639938/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo watch the growth of 1200 \u003cem\u003eP. sylvestris\u003c/em\u003e cv. Negorelskaya trees from seeds to young or even old stage is a big grant project. We want to make a \u0026laquo;seed\u0026ndash;culture\u0026raquo; passport. Each individual seed (N\u0026thinsp;=\u0026thinsp;1200) was weighed, and image acquisition via a flatbed scanner in the VIS wavelength region and seeded into an individual 120 cm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e cell of a 40-cell container. On day 30, container-grown germination was evaluated according to the following dichotomous criterion: 1 \u0026ndash; germinated (n\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;942), 0 \u0026ndash; did not germinate (n\u003csub\u003e0\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;258), and 0-group and 1-group datasets were formed. The RGB space color of the individual seed epidermis between the 0- and the 1-group were compared via the Kolmogorov‒Smirnov criterion D. The lower individual weight of the seed in the 0-group compared with the 1-group was not accidental (p\u0026thinsp;=\u0026thinsp;0.0045). Additionally, in the 0 group, the median values of R, G, and B brightness of pixels from individual seeds are not accidental (p\u0026thinsp;=\u0026thinsp;0.0000381) compared with those of the 1 group. Therefore, in this experiment, seeds that reflected most of the light from the epidermis showed a lower germination when placed in the container.\u003c/p\u003e","manuscriptTitle":"How Germination Changes During Individual Seed RGB-Space Differentiation: The Case of Pinus sylvestris L. сv. Negorelskaya","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-23 05:55:32","doi":"10.21203/rs.3.rs-6639938/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"05137103-4eb1-4c5a-b1d4-d954de07cb6c","owner":[],"postedDate":"July 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":51657038,"name":"Biological sciences/Plant sciences/Plant physiology"},{"id":51657039,"name":"Biological sciences/Plant sciences"},{"id":51657040,"name":"Earth and environmental sciences/Ecology/Forestry"}],"tags":[],"updatedAt":"2025-10-14T07:23:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-23 05:55:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6639938","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6639938","identity":"rs-6639938","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}