Genomics and Applied Biology 2026, Vol.17, No.5, 312-325 http://bioscipublisher.com/index.php/gab 324 information and move toward closed-loop management, although automated recommendation remains an important next step. Future systems will also depend on continuous monitoring and adaptive control.Reinforcement-learning-based digital twins are emerging as promising tools for optimization, automation, and resource management in agriculture, particularly where virtual environment representations can support policy learning for dynamic decision tasks.More broadly, digital farming and twin-based systems are expected to enhance productivity and efficiency through real-time synchronization between physical farms and virtual models, creating a practical foundation for precision medicinal plant agriculture. The most credible long-term outlook is therefore human-centered and trustworthy AI rather than fully autonomous black-box prediction.Agricultural AI must integrate multimodal information, remain robust to small disturbances, and explain its outputs to domain experts if it is to be trusted in biologically and economically sensitive systems. For Zhejiang medicinal plants, next-generation computational medicinal botany will be strongest when explainable AI, multi-omics, environmental sensing, and experimental validation are combined into transparent decision-support systems for sustainable cultivation and reliable quality control. Acknowledgments I extend my sincere gratitude to the anonymous reviewers for their valuable and insightful comments, which have greatly strengthened this paper. Conflict of Interest Disclosure The author affirms that this research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest. References Cacho J., Feinstein J., Zumpf C., Hamada Y., Lee D.J., Namoi N., Lee D., Boersma N., Heaton E., Quinn J., and Negri C., 2023, Predicting biomass yields of advanced switchgrass cultivars for bioenergy and ecosystem services using machine learning, Energies, 16(10): 4168. https://doi.org/10.3390/en16104168 Chen J., Cai J., Duong H.T.Q., Bunsupa S., Han R., and Tong X., 2026, AI-driven integration and optimization of medicinal plant multi-omics metabolic networks, Frontiers in Plant Science, 17: 1756809. https://doi.org/10.3389/fpls.2026.1756809 Cheng S., Hu Y., Cheng Y., Qian Z., Xu X., Lei X., and Shi X., 2026, Variation analysis of growth traits and medicinal components in different provenances of Polygonatum cyrtonema based on heterogeneous garden experiment, PLOS One, 21(4): e0346920. https://doi.org/10.1371/journal.pone.0346920 Dale R., Oswald S., Jalihal A., Laporte M., Fletcher D., Hubbard A.H., Shiu S.H., Nelson A.D., and Bucksch A., 2021, Overcoming the challenges to enhancing experimental plant biology with computational modeling, Frontiers in Plant Science, 12: 687652. https://doi.org/10.3389/fpls.2021.687652 Debbagh M., Sun S., and Lefsrud M., 2025, Predictive modeling, pattern recognition, and spatiotemporal representations of plant growth in simulated and controlled environments: a comprehensive review, Plant Phenomics, 2025: 100089. https://doi.org/10.1016/j.plaphe.2025.100089 Ding R., Luo J., Wang C., Yu L., Yang J., Wang M., Zhong S.-H., and Gu R., 2023, Identifying and mapping individual medicinal plant Lamiophlomis rotata at high elevations by using unmanned aerial vehicles and deep learning, Plant Methods, 19(1): 38. https://doi.org/10.1186/s13007-023-01015-z Gao H., Li X., Wang C., Li Y., Liu T., Chang N., Xu Y., Wang Y., Ren Y., Zhou G., Gao W., Zeng Y., Zhao H., and Li H., 2026, Integrated multiomics profiling elucidates the spatiotemporal metabolic dynamics and regulatory networks of the bioactive components of Trichosanthes kirilowii, Frontiers in Plant Science, 17: 1735703. https://doi.org/10.3389/fpls.2026.1735703 García-Pérez P., Lozano-Milo E., Landín M., and Gallego P.P., 2020, Combining medicinal plant in vitro culture with machine learning technologies for maximizing the production of phenolic compounds, Antioxidants, 9(3): 210. https://doi.org/10.3390/antiox9030210 Khan S., Pathania N., Kumar P., Kumar R., Kumar J., Kumar N., and Sharma A., 2026, Smart farming approaches in medicinal plant cultivation: a review of techniques, benefits, and sustainability, Planta, 263(4): 92. https://doi.org/10.1007/s00425-026-04960-w Latif R., and Nawaz T., 2026, Medicinal plants and human health: a comprehensive review of bioactive compounds, therapeutic effects, and applications, Phytochemistry Reviews, 25(3): 2299-2342. https://doi.org/10.1007/s11101-025-10194-7
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