Molecular Microbiology Research, 2025, Vol.15, No.2, 69-81 http://microbescipublisher.com/index.php/mmr 79 wilt, and resistance to anthracnose into a single variety. For example, the Verticilliumwilt resistance QTL in Sea Island cotton was combined with the leaf spot resistance gene in Upland cotton to successfully breed a strain resistant to both types of diseases (Guo et al., 2022). In addition, some core transcription factors that regulate multiple signaling pathways, such as WRKY and NAC, have the potential to induce multiple disease responses, providing a basis for multiple (Zhang, 2024) 7.2 Potential of genomics and artificial intelligence-assisted seed selection With the development of high-throughput sequencing technology, cotton genome research has entered the era of multi-omics integration. The accumulation of whole genome resequencing, transcriptome, proteome, epigenome and other data provides rich resources for in-depth exploration of the genetic basis of disease resistance traits (Zhao et al., 2023). Through GWAS, eQTL positioning and other means, researchers can more accurately identify key regulatory genes and regulatory networks. Based on this, artificial intelligence (AI) technology has also begun to play a role in disease-resistant seed selection. Modeling and analyzing a large amount of cotton genotype and phenotype data using machine learning algorithms can predict disease resistance potential. Existing studies have established disease resistance trait prediction models based on random forests and support vector machines, with an accuracy rate of more than 80%, which can be used for screening and decision support of early-generation populations. AI also shows great potential in image recognition. Combining drone remote sensing with automatic recognition of cotton field lesion images, the disease occurrence area can be monitored in real time and a precise prevention and control model can be constructed (Maryum et al., 2022). In breeding experiments, AI-assisted image processing technology can be used to quickly count disease symptoms, classify and score, reduce subjective errors, and improve breeding efficiency. In the future, the deep integration of genomics and artificial intelligence will promote the transformation of cotton disease-resistant breeding from "experience-driven" to "data-driven". By establishing a disease-resistant gene library, a phenotypic big data platform, and an intelligent decision-making system, the selection accuracy and breeding speed will be significantly improved (Zhi and Chang, 2021). Especially in cotton breeding with limited resources and long cycles, the assistance of AI and big data will become the key to achieving efficient, low-cost, and sustainable disease-resistant breeding. Acknowledgements We extend our gratitude to Dr. Huang J.Y. for his valuable input in this project. In addition, we thank the two anonymous peer reviewers for their careful review and helpful suggestions. Conflict of Interest Disclosure The authors confirm that the study was conducted without any commercial or financial relationships and could be interpreted as a potential conflict of interest. References Abdelraheem A., Elassbli H., Zhu Y., Kuraparthy V., Hinze L., Stelly D., and Zhang J., 2020, A genome-wide association study uncovers consistent quantitative trait loci for resistance to Verticilliumwilt and Fusariumwilt race 4 in the US upland cotton, Theoretical and Applied Genetics, 133(2): 563-577. https://doi.org/10.1007/s00122-019-03487-x Aini N., Jibril A.N., Liu S., Han P., Pan Z., Zhu L., and Nie X., 2022, Advances and prospects of genetic mapping of Verticillium wilt resistance in cotton, Journal of Cotton Research, 5(1): 5. https://doi.org/10.1186/s42397-021-00109-0 Gao W., Long L., Tian X., Xu F., Liu J., Singh P.K., Botella J.R., and Song C., 2017, Genome editing in cotton with the CRISPR/Cas9 system, Frontiers in Plant Science, 8: 1364. https://doi.org/10.3389/fpls.2017.01364 Koima I.N., Kilalo D.C., Orek C.O., Wagacha J.M., and Nyaboga E.N., 2023, Identification and characterization of Colletotrichum species causing sorghum anthracnose in Kenya and screening of sorghum germplasm for resistance to anthracnose, Journal of Fungi, 9(1): 100. https://doi.org/10.3390/jof9010100
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