Global food security is increasingly at risk due to factors like climate change and population growth, necessitating advancements in agricultural technology. Digital breeding, a method centered around genotype-phenotype selection using next-generation sequencing (NGS), offers a solution by enabling the identification of genetic mutations and predicting crop traits with greater speed and precision compared to traditional approaches. This automated breeding process efficiently gathers and analyzes genotype and phenotype data, improving key traits such as growth, yield, and tolerance while minimizing human intervention. Despite advancements in sequencing technologies, challenges remain due to the high cost and impracticality of acquiring extensive genomic datasets. To address these limitations, this study explores data augmentation strategies using deep learning techniques, focusing on their success in other fields like computer vision. Unlike conventional Generative Adversarial Networks (GANs), which face stability issues, we present a novel approach using a stacked convolutional and LSTM architecture. This model leverages SNP position information to capture correlations within genomic regions and introduces a specialized metric to evaluate the quality of augmented data. The effectiveness of the proposed phenotype prediction model is demonstrated through real-world testing on a collection of 192 tomato varieties, highlighting its potential to revolutionize breeding processes and improve agricultural outcomes.

Food shortages, driven by population growth and climate change, pose a significant global challenge. Addressing this issue requires a dual focus on increasing food production and optimizing land use efficiency. Rather than expanding farmland, current efforts emphasize enhancing crop productivity through plant breeding. Especially, plant breeding research has emerged as a critical solution to the escalating challenges posed by a rapidly growing population and unpredictable climate changes. While plant breeding has a history of contributing to improved crop productivity, addressing current food problems requires the application of innovative technologies. Therefore, digital breeding, which incorporates new technologies, has gained prominence recently. Digital breeding uses high-throughput sequencing technology known as next-generation sequencing (NGS) to decode genome sequences and collect mutation information from diverse individuals. This method selects individuals with specific traits by analyzing the relationship between genotype and phenotype. By leveraging genotype information, digital breeding accurately pinpoints individuals with desired characteristics, significantly expediting the breeding process compared with traditional or molecular breeding methods. The distinctive advantages of digital breeding, such as precise genome selection, make it a promising approach for addressing food security challenges arising from population growth and climate change. Consequently, ongoing research in this domain, particularly combining big data and artificial intelligence technology, further underscores the significance of these advancements. This study explores the significance of artificial intelligence-based genome selection techniques, and associated technologies and outlines future research.