Bioscience Methods 2025, Vol.16, No.2, 108-116 http://bioscipublisher.com/index.php/bm 113 identify new disease-fighting genes more quickly. When researchers combine AI with genetic data, breeding accelerates and yields more insights into how crops display disease resistance (Li, 2024a). The process has been especially helpful in breeding disease-resistant sugarcane. Figure 2 Limitations of the CRISPR/Cas9 system (Adopted from Ahmad et al., 2020) 7 Concluding Remarks Genome strategies have become an indispensable and important means in breeding of disease-resistant sugarcane varieties. The integration of genome selection and genome-wide association analysis (GWAS) greatly promotes the identification of disease-resistant genes and molecular markers, providing key support for the cultivation of disease-resistant varieties. For example, genomic selection has been shown to improve the accuracy of predictions for resistance traits such as brown rust and orange rust, where nonparametric models outperform parametric models, indicating an important role of non-additive genetic effects in disease resistance. In addition, the identification of single nucleotide polymorphism (SNP) sites associated with resistance such as sugarcane mosaic virus (SCMV) and red rot also provides practical pathways for marker assisted breeding. These progress highlights the important role of genomics in accelerating the breeding process and enhancing sugarcane disease resistance. Researchers are using a mix of various techniques to improve sugarcane breeding. By combining genetic studies, gene expression, and computerized machine learning, they're able to identify better how sugarcane defends itself against disease. For instance, research that examined which genes are expressed when the plant is under disease infection found important genes for photosynthesis and stress response that fight sugarcane mosaic virus. Computer programs are also helping to pinpoint precisely where in the sugarcane DNA the disease resistance is encoded so that it can be easily recognized and predicted such factors as brown rust resistance. If scientists collect all this diverse data, they can choose the most appropriate plants to breed much faster, leading to sugarcane varieties that stay healthier.
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