# Exploring Data Augmentation Algorithm to Improve Genomic Prediction of Top-Ranking Cultivars  
[PERSON]  
1 Facultad de Telematica, Universidad de Colima, Colima 28040, Mexico; [EMAIL_ADDRESS] (O.A.M.-L.)  
[PERSON]  
2 Icark--Indian Agricultural Research Institute, Pusa Campus, New Delhi 110012, India; [EMAIL_ADDRESS]  
[PERSON]  
3 Independent Researcher, Zinacetpec 75960, Mexico; [PERSON]RESS]  
[PERSON]  
4 Celegio de Postgraduados Campus Cordoba, Km. 348 Carretera Federal Cordoba-Vercruz, Amatalin de los Reyes, Vercaruz 9446, Mexico; [EMAIL_ADDRESS]  
Afeldabotesinos-Lopez  
5 International Institute of Topical Agriculture (ITA), Ibadan 20001, Nigeria; [EMAIL_ADDRESS]  
[PERSON]  
1 Facultad de Telematica, Universidad de Colima 28040, Mexico; [EMAIL_ADDRESS] (O.A.M.-L.)  
1 Facultad de Telematica, Universidad de Colima 28040, Mexico; osva178@gmail. livestock breeding has encouraged plant breeders to implement GS for predicting inbred performance, aiding parental selection, and forecasting hybrid performance [3; 4]. For these reasons, GS is revolutionizing plant breeding programs by offering significant advantages in terms of accuracy, efficiency, and enabling more precise and effective breeding strategies. This transformative approach enhances yield, improves quality, and boosts resilience to environmental challenges [4; 5]. Countries adopting GS are poised to enhance their food sovereignty by improving productivity, sustainability, and resilience in food production systems while autonomously managing and conserving genetic resources [4; 5]. GS aims to merge comprehensive genotypic and phenotypic data from a training population to develop predictive models [5]. These models are used to estimate genetic values and select individuals within a breeding population based on their genotype data. This method circumvents extensive testing, thereby avoiding biases in marker effect estimates and speeding up the breeding process. The similarity between training and breeding populations is crucial for accurate predictions [5]. Higher accuracies are achieved when the training population closely resembles the breeding population. In contrast, greater genetic distances between the two populations result in rapid decreases in accuracy [3; 6]. The optimal size of a training population depends on relatedness, trait heritability, and population structure. Smaller training populations are ideal for closely related groups, while larger ones are necessary for more distantly related populations [7]. Accurate prediction is key to the successful implementation of GS, as it enables breeders to select individuals with desirable traits for future breeding cycles, thereby increasing genetic gain per cycle compared to marker-assisted selection [8]. Predictability, representing prediction accuracy, has been assessed in crops like maize, wheat, and barley through cross-validation [9; 10]. These studies have shown that predictability is influenced by heritability, relatedness, sample size, marker density, and genetic architecture. Generally, predictability increases with higher marker density and larger sample sizes until it plateaus. The relatedness between training and breeding populations also significantly impacts predictability. Given the complexities of genetics, environmental variations, and data limitations, new methods are needed to improve prediction accuracy in GS. Accurate phenotyping and marker data are essential to minimize prediction errors. Optimizing GS methodology is challenging, as some factors require increased resources while others do not benefit from such increases. However, statistical machine learning models offer a promising area for optimization [11]. Studies comparing various GS models have shown that no single model is best for all traits, with prediction accuracy depending on the number of genes controlling the trait, allele effect distribution, presence of epistasis, and heritability [12].