AI‑DRIVEN MICROCLIMATE PERSONALIZATION IN INDOOR AND URBAN GARDENING
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Abstract
Indoor and urban gardening has expanded rapidly as people seek fresh produce, resilient local food options, and a more sustainable lifestyle. Yet many smart‑garden systems still manage the environment in “zones”, meaning one set of settings is applied to an entire shelf or chamber. Zone control is simple, but it ignores plant‑to‑plant differences in size, age, leaf shape, and water demand, which can lead to uneven growth and wasted resources. Recent advances in artificial intelligence (AI), low‑cost sensors, and “digital twins” (virtual models that mirror real plants and conditions using live data) make it realistic to personalize the microclimate for each plant. This article explains why plant‑level microclimate personalization matters, what technologies now make it possible, and how a practical system could combine sensor data, digital twins, reinforcement learning (a learning‑by‑trial decision method), and circadian‑aware scheduling (timing actions to match a plant’s daily biological rhythm). Drawing on recent greenhouse and indoor‑farming literature, the article proposes a clear evaluation framework: compare static presets, rule‑based automation, and AI‑driven personalization, focusing on outcomes that matter to both home gardeners and urban farms: growth, uniformity, quality signals, energy use, and water use (Chen et al., 2025; Kaiser et al., 2024; Padilla‑Nates et al., 2025; Platero‑Horcajadas et al., 2024).
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1.Chen, S., Liu, A., Tang, F., Hou, P., Lu, Y., & Yuan, P. (2025). A review of environmental control strategies and models for modern agricultural greenhouses. Sensors, 25(5), 1388. https://doi.org/10.3390/s25051388
2.Kaiser, E., Kusuma, P., Vialet‑Chabrand, S., Folta, K. M., Liu, Y., Poorter, H., Woning, N., Shrestha, S., Ciarreta, A., Van Brenk, J., Karpe, M., Ji, Y., David, S., Zhu, X.‑G., Huntenburg, K., Verdonk, J., Woltering, E., Gauthier, P. G., Courbier, S., & Taylor, G. (2024). Vertical farming goes dynamic: Optimizing resource use efficiency, product quality, and energy costs. Frontiers in Science, 2, 1411259. https://doi.org/10.3389/fsci.2024.1411259
3.Kartushinskii, S. A., Trofimov, Y. V., & Lishik, S. I. (2025). Hybrid digital twin for phytotron microclimate control: Integrating physics‑based modeling and IoT sensor networks. AgriEngineering, 7(9), 285. https://doi.org/10.3390/agriengineering7090285
4.Mujahid, M., Ambreen, A., Zarlashat, Y., Sarfraz, Z., Iqbal, M. S., & Waheed, A. (2025). Integration of light and circadian signaling in plant gene regulatory networks: Implications for photomorphogenesis and stress adaptation. Biology, 14(10), 1375. https://doi.org/10.3390/biology14101375
5.Padilla‑Nates, J. P., Garcia, L. D., Lozoya, C., Orona, L., & Cortes‑Perez, A. (2025). Greenhouse irrigation control based on reinforcement learning. Agronomy, 15(12), 2781. https://doi.org/10.3390/agronomy15122781
6.Platero‑Horcajadas, M., Pardo‑Pina, S., Cámara‑Zapata, J. M., Brenes‑Carranza, J. A., & Ferrández‑Pastor, F. J. (2024). Enhancing greenhouse efficiency: Integrating IoT and reinforcement learning for optimized climate control. Sensors, 24(24), 8109. https://doi.org/10.3390/s24248109
7.Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press.