Molecular Plant Breeding 2024, Vol.15, No.1, 27-33 http://genbreedpublisher.com/index.php/mpb 28 2 Innovation in Technology and Methods 2.1 Genome editing and precise genome design Breeding 5.0 will further advance genome editing technologies, such as improvements to the CRISPR-Cas9 system and the introduction of new gene-editing tools. These technologies will enable more precise and efficient genome modifications, including knockout, knockin, gene function correction, or replacement of specific DNA segments (Adem et al., 2017; Alexander, 2018). Precise genome design will become a routine method, allowing breeders to accurately introduce or delete target genes in the plant genome, thereby achieving regulation and optimization of specific traits, creating stronger crop varieties, enhancing drought resistance, disease resistance, and increasing yield. Moreover, this technology will be utilized to improve the nutritional value of crops and enhance their ability to adapt to environmental conditions (Vu et al., 2021). 2.2 Application of artificial intelligence and big data in breeding Breeding 5.0 will extensively employ artificial intelligence and big data analysis technologies. Through methods such as machine learning, deep learning, and data mining, we can better analyze and interpret massive amounts of plant genetic and phenotypic data. Artificial intelligence algorithms will assist in discovering potential gene-trait associations, predicting plant traits and quality, and optimizing breeding strategies. The application of big data will also expedite the breeding process, enhance selection efficiency, and provide a scientific basis for breeding decisions. In fact, artificial intelligence and big data have already demonstrated significant potential in the field of breeding. For instance, modern plant breeding relies on genomic and phenotypic selection, involving the analysis of large datasets. Artificial intelligence plays a crucial role in handling these complex datasets, particularly in high-throughput phenotypic analysis, genomic selection, and environmental data analysis (Khan et al., 2022). Additionally, leveraging artificial intelligence and big data analysis can expedite breeding programs, especially in linking genotype to phenotype. Such integration can rapidly identify key genes, thereby accelerating crop improvement processes (Harfouche et al., 2019). Big data analysis has enhanced the accuracy of predicting complex traits in crop breeding. Using a large amount of genotype and phenotype information can more accurately predict crop yield and other important traits (Singh and Prasad, 2021). Furthermore, advancements in artificial intelligence have opened up new possibilities for breeding. Natural language processing models, such as ChatGPT, can be utilized to parse and comprehend vast amounts of scientific literature and research findings, providing breeders with broader knowledge and information. These intelligent tools can assist breeders in decision-making and predictions, offering more accurate and comprehensive breeding recommendations. 2.3 Application of multi-omics integration and systems biology approaches Breeding 5.0 will further advance the application of multi-omics integration and systems biology approaches in breeding (Kuriakose et al., 2020). Integrating various levels of omics data, such as genetics, epigenetics, transcriptomics, metabolomics, etc., can comprehensively unravel the relationships between plant traits and the genome. Systems biology approaches, such as network analysis and metabolic pathway modeling, will aid in a deeper understanding of plant biological processes and interaction networks, offering new strategies and insights for achieving breeding goals. Multi-omics approaches play a crucial role in elucidating the growth, aging, yield, and responses to biotic and abiotic stresses of crops. These methods have been applied in important crops such as wheat, soybeans, tomatoes, emphasizing the relationship between crop genomes and phenotypes through the integration of functional genomics with other omics data (Yang et al., 2021).
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