Bioscience Evidence 2026, Vol.16, No.4, 304-315 http://bioscipublisher.com/index.php/be 313 With the rapid development of smart agriculture, multi-sensor integration systems have been increasingly applied in facility crop production. IoT technologies can continuously collect environmental data through sensors and optimize irrigation, greenhouse conditions, and production management through data analysis (Mansoor et al., 2025). However, intelligent agriculture still faces challenges related to equipment cost, data management, and technical accessibility. For specialty medicinal plants such as D. officinale, intelligent development should not simply focus on complex and expensive systems but should first address the most important production problems. Future intelligent production of D. officinale can mainly develop in three directions. First, low-cost sensors can be used to monitor temperature, humidity, light intensity, and substrate moisture, enabling digital recording of production environments. Second, environmental and quality prediction models can be developed based on historical production data to provide decision support for cultivation management. Third, environmental monitoring, disease warning, and quality evaluation can be integrated to establish a complete production management system. Artificial intelligence technologies have already been applied to growth status recognition in orchid plants, providing technical support for future intelligent management of D. officinale (Chen et al., 2022). Overall, intelligent cultivation is not intended to completely replace human management but to improve the accuracy of production decisions. For practical production, especially at the grower level, a combination of “human experience + digital tools” may be more suitable. Through simple and reliable data collection and analysis, environmental management can be gradually improved, supporting stable production of high-quality D. officinale. 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 Chen J., Zhang Z., Liu Y., Duan X., Zhang M., Jiang W., and Tao Z., 2026, Breeding study of a new variety of Dendrobium officinale 'Tiefeng No. 1', Horticulturae, 12: 326. https://doi.org/10.3390/horticulturae12030326 Chen L. B., Huang G. Z., Huang X. R., and Wang W. C., 2022, A self-supervised learning-based intelligent greenhouse orchid growth inspection system for precision agriculture, IEEE Sensors Journal, 22: 24567-24577. https://doi.org/10.1109/JSEN.2022.3221960 Chen W. H., Wu J. J., Li X. F., Lu J. M., Wu W., Sun Y. Q., Zhu B., and Qin L. P., 2021b, Isolation, structural properties, bioactivities of polysaccharides from Dendrobium officinale Kimura et Migo: A review, International Journal of Biological Macromolecules, 184: 1000-1013. https://doi.org/10.1016/j.ijbiomac.2021.06.156 Chen W., Lu J., Zhang J., Wu J., Yu L., Qin L., and Zhu B., 2021a, Traditional uses, phytochemistry, pharmacology, and quality control of Dendrobium officinale Kimura et Migo, Frontiers in Pharmacology, 12: 726528. https://doi.org/10.3389/fphar.2021.726528 Cheng J., Dang P. P., Zhao Z., Yuan L. C., Zhou Z. H., Wolf D., and Luo Y. B., 2019, An assessment of the Chinese medicinal Dendrobium industry: Supply, demand and sustainability, Journal of Ethnopharmacology, 229: 81-88. https://doi.org/10.1016/j.jep.2018.09.001 Ding J. T., Tu H. Y., Zang Z. L., Huang M., and Zhou S. J., 2018, Precise control and prediction of the greenhouse growth environment of Dendrobium candidum, Computers and Electronics in Agriculture, 151: 453-459. https://doi.org/10.1016/j.compag.2018.06.037 Du G., Zhao Y., Xiao C., Ren D., Ding Y., Xu J., Jin H., and Jiao H., 2023, Mechanism analysis of calcium nitrate application to induce gibberellin biosynthesis and signal transduction promoting stem elongation of Dendrobium officinale, Industrial Crops and Products, 195: 116495. https://doi.org/10.1016/j.indcrop.2023.116495 Guo X., Lin Z., Zhou L., Xu Q., Li M., Yuan F., Wang J., Cai L., Zhang Z., and Gu L., 2025, Integrated phenotypic, physiological, and transcriptomic analyses reveal light-intensity regulatory mechanisms underlying the multifunctional trait improvement in Dendrobium officinale, Plant Physiology and Biochemistry, 229: 110713. https://doi.org/10.1016/j.plaphy.2025.110713 He Q., Lu A., Qin L., Zhang Q., Lu Y., Yang Z., Tan D., and He Y., 2022, An UPLC-Q-TOF/MS-based analysis of the differential composition of Dendrobium officinale in different regions, Journal of Analytical Methods in Chemistry, 2022: 8026410. https://doi.org/10.1155/2022/8026410
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