Molecular Pathogens, 2025, Vol.16, No.3, 134-146 http://microbescipublisher.com/index.php/mp 144 directionally between sugar cane rows to achieve accurate application of pest and pest hot spots. Precise drug administration also includes targeted agents and administration methods for different diseases and pests. For underground pests, intelligent fertilization seeds can be used to quantitatively apply granules to the rhizosphere; for locally occurring leaf diseases, AI can be used to identify and locate lesions and perform point spray treatment, thereby reducing agent waste and environmental load. 8.3 The role of agricultural internet of things and big data analysis in prevention and control decision-making The Internet of Things in the agricultural Internet of Things provides massive real-time data for big data analysis and decision-making support by connecting sensing nodes such as insect conditions, disease conditions, meteorological soil in the field into the network. Building an Internet of Things platform for sugarcane pest control can realize digital management of the entire prevention and control process. On the one hand, real-time data obtained by the Internet of Things perception layer is transmitted and aggregated, and can be used for pest and disease occurrence prediction and epidemic simulation. For example, by conducting big data analysis of years of sugarcane insect situation data and climate data, the laws and key drivers of pest outbreaks can be mined and predictive models can be trained for future forecasting. The introduction of artificial intelligence algorithms has greatly improved the prediction accuracy. It is reported that the identification of field image data through deep learning models can diagnose major sugarcane diseases and evaluate their severity with high accuracy. On the other hand, big data also assists in formulating optimal prevention and control strategies. Based on the database analysis of the historical effects and costs of different prevention and control measures, the decision-making system can provide the best combination of prevention and control plans under the current insect situation. With the support of big data systems, traditional plant protection decisions that rely on experience are changing towards scientific and intelligent. China has begun to build a national digital management platform for crop diseases and pests, collect and analyze monitoring data from various places, and issue pest warnings and prevention and control guidance. For crops like sugarcane, the role of big data decision-making is particularly prominent: it can achieve accurate timing selection and drug optimization for regional unified prevention, thereby reducing disease and insect populations on a larger scale. Acknowledgments The authors would like to thank Mr. Huang from Cuixi Biotechnology Research Institute for his revisions, as well as the careful review and pertinent suggestions of anonymous review experts. 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 Almeida J.E.M., 2019, Microbial control of sugarcane pests, Natural Enemies of Insect Pests in Neotropical Agroecosystems, 427-436. https://doi.org/10.1007/978-3-030-24733-1_34 Campbell P., Mcfarlane S., Antwerpen T., Antwerpen R., Rhodes R., McElligott D., Berry S., Leslie G., Rutherford R., and Conlong D., 2009, An investigation of ipm practices for pest control in sugarcane, Sugar Tech, 2(3): 1-8. De Oliveira J., and Reigada C., 2023, Functional response and parasitism rate of Trichogramma galloi Zucchi (Hymenoptera: Trichogrammatidae) a parasitoid of eggs of sugarcane borer, Neotropical Entomology, 52: 725-730. https://doi.org/10.1007/s13744-023-01046-0 Filho F.H., Kong Z., Heldens W.B., and De Lange E.S., 2019, Drones: innovative technology for use in precision pest management, Journal of Economic Entomology, 113(1): 1-25. https://doi.org/10.1093/jee/toz268 Gao Y.J., 2013, Screening of SSR marker linked to smut resistance gene in sugarcane, Chinese Journal of Tropical Crops, 34: 2222-6. Giannoulis A., Mistriotis A., Papardaki N., and Briassoulis D., 2021, Evaluation of insect-proof agricultural nets with enhanced functionality, Biosystems Engineering, 208: 98-112. https://doi.org/10.1016/J.BIOSYSTEMSENG.2021.05.012 Huang W.J., Sun D.L., An Y.X., Lu Y.L., and Chen L.J., 2021, Impact of pesticide/fertilizer mixtures on the rhizosphere microbial community of field-grown sugarcane, 3 Biotech, 11(5): 210. https://doi.org/10.1007/s13205-021-02770-3
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