Search Results (317)

Genetic background strongly influences the penetrance and phenotypic expression of SMAD4-associated intestinal tumorigenesis, yet the underlying modifier loci remain poorly defined. To investigate the genetic architecture of tumor susceptibility and systemic physiology, we analyzed 260 Smad4⁺/⁻ × Collaborative Cross (CC)-F1 mice derived from 14 CC lines using 11 quantitative traits, including longitudinal body weight, adjusted organ weights, and intestinal polyp counts across anatomical regions. Principal component analysis reduced these traits to seven components explaining more than 85% of total phenotypic variance. PC1 represented a tumor burden–metabolic axis, whereas PC2 captured systemic organ-physiology variation. Genome-wide QTL mapping of principal component scores identified significant loci for PC1 on chromosomes 1 and 4 and a female-specific locus for PC5 on chromosome 10, with additional suggestive loci supporting a polygenic architecture. Founder-effect analysis revealed strong contributions from CAST/EiJ, 129S1/SvImJ, and WSB/EiJ haplotypes. Candidate gene annotation identified biologically relevant coding and noncoding loci, including Galnt7 and Galntl6, as well as regulatory regions with potential enhancer activity. Together, these findings indicate that intestinal tumor susceptibility in Smad4⁺/⁻ × CC-F1 mice is influenced by multiple coding and regulatory genetic modifiers with sex-dependent effects. This study demonstrates that integrating multivariate phenotyping with systems genetics analyses provides an effective framework for identifying the complex genetic networks underlying intestinal tumorigenesis and associated systemic physiological variation in genetically diverse mouse populations. Full article

In population health the highly cited Atherosclerosis Risk in Communities study indicated a U-shaped association between carbohydrate intake and mortality, whilst the Prospective Urban Rural Epidemiology study linked higher fat intake to lower mortality risks. The Malaysia Lipid Study reported high-fat and high-carbohydrate dietary patterns were associated with increased cardiometabolic risks, including insulin resistance and small dense LDL particles generation. This animal model study therefore was purposely designed to evaluate metabolic outcomes of carbohydrate–fat permutations in Zucker diabetic fatty (ZDF) and Zucker lean (Z_lean) rats by using ¹H Nuclear Magnetic Resonance (NMR) metabolomics. Twenty-four ZDF rats were randomly divided into four groups (n = 6 per group): control (standard diet), Diet A (54%-energy carbohydrate, 32%-energy fat, 14%-energy protein) mimicking a recommended adult Malaysian diet, Diet B (49%-energy carbohydrate, 37%-energy fat, 14%-energy protein) mimicking a low-carbohydrate, moderate high-fat diet, and metformin treatment (100 mg/kg), which effectively represents a 5%-energy exchange in isocaloric meals. An additional six Z_lean were given Diet A (n = 3) and Diet B (n = 3). The intervention lasted eight weeks. Using log-transformed data, analysis of variance (ANOVA) revealed significant differences between the groups for eleven metabolites (1,6-anhydro-β-D-glucose, 2-hydroxyvalerate, acetate, 3-aminoisobutyrate, 3-hydroxybutyrate, carnitine, choline, citrate, creatine, lactate, and N-methylhydantoin) (all p < 0.05) which remained significant even after false discovery rate (FDR) correction. Majorly elevated metabolites in both ZDF and Z_lean rats were 3-hydroxybutyrate, N-methylhydantoin, 3-aminoisobutyrate, and carnitine, indicating dietary influences independent of diabetes status. Conversely, citrate and 1,6-anhydro-β-D-glucose levels showed distinct patterns across the groups, with Z_lean rats exhibiting lower levels and ZDF rats showing higher levels compared to healthy controls, suggesting potential genetic or physiological influences. Other metabolites such as creatine, acetate, choline and 2-hydroxyvalerate showed varied trends, highlighting metabolic complexities. Compared to the control group, metformin treatment generally resulted in lower levels of metabolites, except for acetate, which was higher, indicating improved insulin sensitivity. The findings indicated moderate high-fat diets may exacerbate metabolic disturbances as seen in both ZDF and Z_lean rats, while metformin treatment generally improved metabolic profiles. Full article

The growing demand for high-quality beef has driven the selection of bovine genotypes offering superior marbling and tenderness. This study evaluated meat quality parameters in heifers from four Nellore-based genetic groups: ½ Aberdeen Angus × ½ Nellore (AN), ½ Japanese Brown × ½ Nellore (BN), ½ Japanese Brown × ¼ Aberdeen Angus × ¼ Nellore (TriBAN), and ½ Japanese Black × ¼ Aberdeen Angus × ¼ Nellore (TriJAN). Thirty-two heifers (eight/group) were finished in commercial feedlots. Samples of the longissimus thoracis muscle were analyzed for physical attributes (pH, cooking loss, shear force, marbling, water retention capacity, and colour), chemical composition, and fatty acid profile. In addition, animal performance (daily gain and final weight) was determined. A completely randomized design was used with analysis of variance followed by Tukey’s test (α = 0.05). The AN group showed the greatest final weight and daily gain (p < 0.01). There were no significant differences among genetic groups for pH, cooking loss, shear force, marbling, water-holding capacity, or colour (p > 0.05). Across all groups, the mean ultimate pH was 5.59, shear force (WBSF) averaged 41.11 N, and intramuscular fat (ether extract) content averaged 13.15 g 100 g⁻¹, indicating desirable technological and eating-quality characteristics. Most fatty acids did not differ among treatments (p > 0.05), except for arachidonic acid (C20:4 n-6), which was present at higher concentrations (p < 0.05) in the TriBAN group than in the other genetic groups. In conclusion, despite differences in productive performance, all genotypes produced meat with consistent quality traits, including tenderness, marbling, and fatty acid profiles associated with high-quality beef. Full article

It is well known that genetic variants contribute to cellular sensitivity to chemotherapeutic agents and ionizing radiation (IR). The aim of this study was to identify single nucleotide polymorphisms (SNPs) and genes associated with the spectrum of normal cellular sensitivity of lymphoblastoid cell lines (LCLs) towards ionizing radiation and mitomycin C (MMC). In the first step, we determined the viability of LCLs established from male participants of the Berlin Aging Study II (BASE-II) aged ≥62 years following treatments with increasing doses of IR (n = 137 cell lines) or MMC (n = 140 cell lines) using the alamarBlue assay. Results from intra-experimental triplicates and three independent experiments for each cell line and treatment were used to calculate the area under the curves (AUCs) representing the specific sensitivity to IR and MMC of each LCL. The data from these experiments were subsequently used as outcomes in genome-wide association studies (GWASs). In addition, we calculated polygenic risk scores (PGS) from UK Biobank GWAS results for four cancer-related phenotypes and assessed the extent to which the variance in the IR and MMC sensitivity is explained by these PGS. The GWAS analyses revealed one variant, rs74728080, located in CDH13 on chromosome 16, to show genome-wide significant (p < 5 × 10⁻⁸, ß = 2.81) association with cellular viability after treatment with IR. In the GWAS on MMC sensitivity the most interesting signal was elicited by SNP rs113978558 in an intron of the PLD5 gene on chromosome 1 (p = 9.232 × 10⁻⁸; ß = 1.44). Several other SNPs with statistically suggestive (i.e., p < 1 × 10⁻⁵) evidence of association with IR or MMC sensitivity were identified. PGS calculations from GWAS of four cancer-related traits in UKB explained ~5% and ~3% of phenotypic variance in IR- and MMC-induced cell viability, respectively. The genome-wide significant association of rs74728080 with IR sensitivity and the location of this variant in CDH13 is interesting and functionally highly plausible given its known involvement in oxidative stress response and function as a tumor suppressor. Taken together, our novel data suggest that CDH13 may be genuinely involved in regulating cellular IR sensitivity. Full article

Rice ratooning is a widely adopted practice in southwest Louisiana because it allows production of an additional crop from the existing rice stand with relatively low input requirements. Although ratoon-crop yield is generally lower than main crop yield, the additional harvest can improve overall system productivity and profitability due to reduced establishment and production costs. This study evaluated the effect of seeding rates on ratoon yield using data collected from 2016 to 2021 across nine seeding rates ranging from 13.0 to 104.3 kg ha⁻¹, 18 rice varieties, and 37 trials. Linear mixed-effects models were used with seeding rate and plant density as fixed effects and year and variety as random effects. Ratoon yield increased significantly with seeding rate (R = 0.285, p < 0.001), with an estimated gain of 7.48 kg ha⁻¹ for each 1 kg ha⁻¹ increase in seeding rate. The estimated optimum seeding rate was 72 kg ha⁻¹; however, ratoon yield responses remained relatively broad near the optimum. Therefore, a practical seeding rate range of 50–80 kg ha⁻¹ is recommended to maximize ratoon yield while accounting for environmental and varietal variability. Main crop yield was not correlated with ratoon yield (R = −0.044). Variance component analysis showed that year and variety contributed substantially to overall yield variability. These results indicate that ratoon yield is significantly influenced by seeding rates, with moderate to high seeding rates supporting greater ratoon productivity, while environmental and genetic factors also play important roles. Full article

Reproductive efficiency is a key factor in meat rabbit production; however, estimating variance components for litter size can be challenging in populations with limited data. Our objective in this study was to estimate genetic parameters for litter size in meat rabbits using frequentist and Bayesian approaches. A total of 956 kindling records were analyzed, with approximately 40–48% of does contributing only a single record. The traits analyzed were the number of kits born alive, stillborn, and total born, and litter size at 7, 35, and 70 days. Variance components were estimated using restricted maximum likelihood (REML) and Gibbs sampling as implemented in the BLUPF90 suite of programs. Univariate and bivariate mixed models were fitted, including additive genetic effects, with and without the permanent environmental effect. Heritability estimates were low for all traits (<0.10), with slightly higher estimates under the Bayesian approach. Including the permanent environmental effect improved model fit but caused convergence problems with REML, whereas the Bayesian approach yielded non-zero estimates and an explicit characterization of uncertainty through posterior distributions. Although overall correlations between predicted breeding values were high, minor changes in animal rankings were detected when comparing full and reduced models under the Bayesian approach. These results highlight the importance of inference methods and model specification in genetic evaluations based on limited-information datasets. Full article

The elite maize inbred line Zheng58, female parent of the widely cultivated hybrid Zhengdan958, is renowned for conferring short stature and high-density tolerance. Despite its critical role in modern breeding, the genetic basis of its dominant dwarfing effect has remained elusive. In this study, we dissected the genetic architecture of six plant architecture traits in a recombinant inbred line (RIL) population derived from elite inbred lines Zheng58 and PH6WC. Phenotypic evaluations across four environments revealed high heritability with additive effects accounting for 45.5–64.6% of the total genetic variance. A total of 125 QTLs were identified for the traits in single-environment QTL mapping, and multi-environment analysis further detected 77 QTLs for the six traits. Notably, a major dominant-negative pleiotropic QTL was identified on chromosome 2 that consistently explains plant height (PH), plant height above ear (PHAE), and average internode length above ear (AILAE) across multiple environments. The Zheng58 allele at this locus acts dominantly to reduce plant height by approximately 8.7 cm, providing a genetic explanation for Zheng58’s characteristic dwarfing effect. Regional association mapping refined this QTL to a 351.9 kb interval harboring 13 candidate genes. Transcriptome analysis uncovered 205 differentially expressed genes (DEGs) within the QTL region, with only one DEG (Zm00001d005848) located in the pleiotropic hotspot QTL on chromosome 2. This candidate gene encodes a rhomboid protease homolog, and population-wide expression data showed significantly negative correlation with PH. Our study unveils the genetic mystery of Zheng58′s dominant dwarfing phenotype by pinpointing a pleiotropic QTL hotspot, offering a strategic target for molecular breeding of compact, high-density-tolerant hybrids in maize. Full article

Fertility and hatchability are key reproductive traits affecting the efficiency and sustainability of poultry production; however, their genetic improvement remains challenging because of low heritability and complex biological control. In this study, we estimated the genetic parameters and compared the pedigree- and genomic-based prediction models for fertility rate (FER), hatchability of fertile eggs (HOF), and hatchability of eggs set (HOS) in Thai native chickens. In total, 7075 egg records from 1558 animals were analyzed, including pedigree data for 2646 individuals and genotypes for 400 animals. Prediction performance was evaluated using pedigree-based best linear unbiased prediction (PBLUP), single-step genomic BLUP (ssGBLUP), and weighted ssGBLUP (WssGBLUP). Additive heritability estimates for all traits ranged from low to moderate (0.051–0.068), indicating that environmental factors play an important role in the expression of these traits. Across all traits, WssGBLUP achieved the highest accuracy (0.647–0.648) and showed improved dispersion close to unity, indicating better model stability. Compared with PBLUP, WssGBLUP increased the prediction accuracy by 30–39%, respectively, whereas ssGBLUP outperformed PBLUP by approximately 14–22%. Genome-wide association analysis identified 65 candidate genes across multiple chromosomes, with a strong enrichment of significant signals on the Z chromosome, thus highlighting the role of sex-linked genetic variation. Individual loci explained small proportions of variance, confirming the polygenic nature of these traits. These findings demonstrate that weighted genomic approaches can substantially improve the accuracy and reliability of genetic evaluations for reproductive traits. Overall, this study provides practical support for implementing genomic selection in Thai native chickens, potentially contributing to enhanced reproductive performance, genetic progress, and sustainable poultry production. Full article

Tomato is an important vegetable crop suited to both open-field and protected cultivation. Indeterminate genotypes with high yield potential and desirable quality traits are especially suited to off-season production under protected cultivation. The present study evaluated 57 indeterminate tomato genotypes over two consecutive years under protected conditions to assess genetic variability, genetic divergence, and trait associations across 16 important yield-attributing and quality traits. The analysis of variance depicted significant differences among genotypes for all traits under study. The traits, viz., fruit weight and number of fruits per cluster, exhibited high heritability and high genetic gain, suggesting the predominance of additive gene action and the possibility of direct selection. A significant, positive correlation between fruit weight and the number of plant clusters and yield was observed. Analysis of genetic divergence following Mahalanobis D² statistics classified the genotypes into seven clusters. The number of flowers per cluster and fruit width were the top contributors to the total genetic divergence. Cluster VI outperformed for earliness and yield, Cluster V outperformed for nutritional quality, while Cluster VII was superior for fruit size. Principal Component Analysis revealed that the first five components cumulatively explained 83.3% of the total variation, with PC1 defined by fruit number trait and PC2 by yield and earliness traits. The Multi-Trait Genotype-Ideotype Distance Index (MGIDI) was used to select the best-performing genotypes, highlighting PIDGT-39, PIDGT-42, and PIDGT-29 as elite. Thus, the findings of the present study provide deeper insights into the genetic makeup of indeterminate tomato genotypes and potential parental accessions for tomato improvement, to enhance yield and quality under protected conditions. Full article

Mungbean is an important legume crop native to India. In this study, 500 indigenous mungbean accessions collected from diverse eco-geographical regions of India were evaluated for agronomic trait genetic variability and core collection development. The accessions were grown in an augmented design during 2019 and 2020, and data were recorded for seven quantitative and 13 qualitative traits. Analysis of variance (ANOVA), frequency distribution, and box-plot analyses revealed substantial phenotypic variation among the accessions. Traits including plant height (PHT), number of pods per plant (NPP), hundred-seed weight (HSW), and single-plant yield (SPY) exhibited high heritability coupled with high genetic advance, indicating the predominance of additive genetic effects. Principal component analysis showed that the first three principal components explained 70% of the total phenotypic variation. The Shannon–Weaver diversity index further indicated high levels of genetic diversity within the population. Based on quantitative traits, the accessions were grouped into six major clusters and 42 sub-clusters, with SPY, NPP, HSW, PHT, and days to 50% flowering (DFF) contributing substantially to genetic divergence. Correlation analysis suggested that direct selection for SPY and indirect selection through associated traits, including NPP, HSW, PHT, NSP, and pod length (POL), may enhance yield improvement. The germplasm collection also possessed desirable traits such as high yield potential, contrasting maturity groups, and plant types suitable for mechanical harvesting and bold-seeded type. A representative core set comprising 50 accessions was developed using the PowerCore program, providing valuable genetic resources for mungbean breeding and genetic improvement programs. Full article

Based on SLAF-seq technology, 174 white clover accessions were analyzed using population structure and genetic evolution to develop SNP markers of all accessions. We obtained 2329.4 Mb reads of sequenced data, and the reads of the samples ranged from 4,701,984 to 31,540,232. The sequencing quality value (Q30) uniformly changed from 90.61% to 96.82%, with an average of 93.11%. The GC content of the samples changed from 38.96% to 43.98%, averaging 40.96%, with a control of 34.21%. A total of 320,417 SLAF tags were developed, with an average sequencing depth of 16.42×. There were 202,625 polymorphic SLAF tags, accounting for 63.24% of the total number of SLAF tags. Finally, 2,999,555 polymorphic SNPs were found, and 102,025 high-quality SNPs were selected for downstream analyses after filtering with minor allele frequency (MAF) > 0.05 and completeness > 0.5. Population structure analysis supported K = 2, indicating two major ancestral genetic backgrounds among the accessions. Phylogenetic analysis and principal component analysis further divided the accessions into three genetic subclusters, suggesting finer-scale genetic differentiation. In addition, one-way ANOVA and chi-squared tests revealed a significant association between genetic groups and geographic origin (χ² = 25.78, df = 8, p = 0.0012; F = 3.489, p = 0.032), provided limited evidence for a possible association between genetic grouping and geographic origin. Compared with photosynthetic traits, agronomic traits showed a broader range of variations, with coefficient of variance values for agronomic traits ranging from 24.59% to 139.02% and for photosynthetic traits from 4.29% to 78.57%. This difference suggests that morphological traits were highly differentiated among the 174 accessions. The consistency between phenotypic clustering (based on agronomic traits) and molecular clustering (based on SNP data) suggests that our SNP dataset captures biologically meaningful genetic variation, providing a solid foundation for future genome-wide association studies (GWASs) and marker-assisted selection (MAS) in white clover. Full article

The genetic parameters at 6, 9 and 12 years were studied in two progeny trials (one half-sib and one full-sib) of silver fir (Abies alba Mill.) in Romania, in order to establish an appropriate breeding strategy for advancing second-generation seed orchards. The half-sib trial (HS) consists of 60 open-pollinated families of plus trees from four first-generation seed orchards, while the full-sib trial (FS) consists of 51 half-diallel crosses of 11 plus trees from one seed orchard. Tree height and diameter were found to be under moderate to strong genetic control at both the family and individual levels. Total height showed a higher percentage of additive genetic variance than diameter in both types of progenies. Additive genetic variances increased with age for the diameter (from 12% to 36%), while for the total height, it decreased (from 76% to 35%). In the HS trial, family heritability was higher than individual heritability for both traits. The highest values of heritability were obtained for total height, both at the individual (0.76–0.35) and family levels (0.88–0.63). In FS progenies, the estimates of the narrow-sense individual heritability were lower than those at the family level and remained almost constant over time. The additive age-age genetic correlations and genetic correlations among growth traits were more stable and stronger in FS progeny than in HS progenies. Expected genetic gains were calculated at individual and family levels for different breeding strategies. The highest genetic gain will be obtained through selection of the best parents. Genetic gain slightly varied over age and for progeny tests. The level of genetic diversity, calculated for selected parents based on the breeding values, was high, while the inbreeding coefficient reduced. Combining the backward selection strategy with SSR analyses allows optimization for seed orchard design in order to mitigate inbreeding depression risks and enhance genetic diversity in the next breeding generation. Full article

Genetic diversity analysis can contribute to comparing the relationships between different germplasm resources. Self-recombination is one of the main strategies for the innovation of germplasm resources. In this study, a total of 588 accessions, including two parents and their 586 F_2:4 recombinant individuals originated via the single seed descent (SSD) method, were used to explore the genetic diversity of 17 phenotypic traits. The results indicated that most traits of the recombinants represented continuous distribution and transgressive segregation, with their minimum and maximum values exceeding the parental ranges. Correlation analysis shows that 17 phenotypic traits could be roughly divided into three clusters. There was a significant correlation between traits in the same cluster, such as primary stem height, plant height, and plant canopy diameter in Cluster I; transverse diameter of fruit, fruit shape of apex, node pubescence density, and lamina transverse section morphology in Cluster II; and internode anthocyanin pigmentation, immature fruit color, and leaf color in Cluster III, respectively. The 586 recombinant individuals and two parents were generally clustered into three groups, Group I, Group II, and Group III, which contained 320, 226, and 42 recombinants, respectively. In addition, six principal components were extracted from the 17 phenotypic traits, which could explain 62.97% of the cumulative variance contribution. Importantly, ten recombinants with both purple and long fruit were screened as breeding materials. Overall, this study provides useful information and breeding materials for the utilization and innovation of pepper germplasm resources as well as genetic improvement of pepper. Full article

Background: The immune mechanism of diabetic kidney disease (DKD) has not yet been fully elucidated. This study aimed to characterize circulating lymphocyte subsets in patients with type 2 diabetes mellitus (T2DM), with a particular focus on DKD-related immune alterations and prognosis. Methods: Circulating T cells, B cells and NK cells were identified by flow cytometry. The primary endpoint was all-cause mortality, and overall survival was defined as the time from enrollment to death from any cause or last follow-up. Associations between lymphocyte subsets, inflammatory indices and renal function parameters were analyzed. Cox regression was used to identify factors associated with overall survival in patients with DKD and in the whole T2DM cohort. A prognostic nomogram was developed in the whole T2DM cohort to estimate 1-, 2-, 3-, and 5-year overall survival (OS) probabilities. Model performance was evaluated using the concordance index (C-index), calibration curves, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). Mendelian randomization (MR) was performed as a further exploratory analysis to assess whether immune-related traits were genetically associated with DKD susceptibility, with inverse variance weighting (IVW) as the primary analytical method. Results: In total, 74 T2DM patients were divided into DKD (stage 3–4 of chronic kidney disease) and non-DKD groups. Median follow-up duration was 34.6 months. DKD patients exhibited elevated levels of NK cells, the monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR). In patients with DKD, higher PLR and serum creatinine (SCr) were associated with poorer overall survival, whereas CD4⁺CD25⁺ T cell frequency was not significant after adjustment. In the whole T2DM cohort, higher frequency of circulating CD4⁺CD25⁺ T cells were associated with improved survival (HR 0.920, 95% CI 0.858–0.986, p = 0.019), whereas elevated PLR and SCr were linked to poorer outcomes. The exploratory nomogram incorporating CD4⁺CD25⁺ T cells, PLR, and SCr, showed acceptable internal performance in this cohort. As a separate exploratory analysis, MR suggested that genetically proxied CD4 expression on activated CD4 regulatory T cells was associated with a lower risk of DKD. Conclusions: DKD was associated with higher mortality and elevated MLR-, NLR-, PLR-, and NK cell levels in patients with T2DM. In patients with DKD, PLR and SCr were associated with overall survival, supporting the prognostic relevance of systemic inflammation and renal dysfunction. Individual lymphocyte subsets were not independently associated with survival in the DKD cohort after adjustment, whereas CD4⁺CD25⁺ T cell frequency provided additional prognostic information in the whole extended T2DM cohort analysis. Further validation is warranted. Full article

Peppers from the Capsicum genus are highly valued worldwide for their culinary, medicinal, and nutritional uses. However, accurately classifying and developing new varieties to enhance these traits remains a challenge due to the limitations of traditional methods, which often lack precision and are time-consuming. This study aimed to overcome these limitations by applying advanced multivariate statistical techniques and machine learning models (KNN, RF, XGBoost) to characterize and classify Capsicum genotypes based on genetic and phenotypic features. Sixteen Capsicum genotypes were analyzed using methods such as MANOVA, PCA, and cluster analysis to explore their variabilities and similarities. Cluster analysis revealed the formation of distinct groups, indicating phenotypic similarity patterns among specific varieties. The machine learning models were evaluated using Leave-One-Out cross-validation to address the challenges posed by small datasets. The results indicated that Random Forest outperformed the other models, exhibiting superior class discrimination with an AUC of 0.96, while KNN and XGBoost achieved AUC values of 0.95 and 0.85, respectively. Despite the slightly superior performance of Random Forest relative to KNN, both models demonstrated strong predictive performance, whereas XGBoost exhibited moderate performance. In addition, key agronomic traits such as pericarp thickness, fruit diameter, seeds per fruit, and corolla color were identified as the most relevant variables for classification. Principal component analysis indicated that the first components explained a substantial proportion of the total variance, supporting efficient dimensionality reduction and pattern recognition. Furthermore, the Random Forest model achieved high overall performance, with accuracy, precision, recall, and F1-score values close to 0.93, reinforcing its robustness in multiclass classification. This study highlights the effectiveness of machine learning in overcoming the constraints of traditional classification methods, providing a robust approach for the accurate identification and improvement of pepper varieties. Full article