From Climate Control to Crop Reproducibility: An Intelligent IoT System for Vertical Horticulture

Ensuring experimental reproducibility and reliable isolation of crop responses remain critical challenges in vertical farming and controlled-environment horticulture, where minor microclimatic fluctuations can mask treatment effects and compromise comparability across experiments. This study presents an intelligent, low-cost IoT-based climate management system designed as a methodological framework to stabilize environmental conditions and support reproducible crop responses in vertical horticulture. The system integrates real-time multi-sensor monitoring of temperature, relative humidity, atmospheric pressure, and CO2 concentration with automated high-power actuation for lighting and ventilation within a unified control framework. The platform was validated using lettuce (Lactuca sativa L. cv. Ofelia) cultivated under controlled vertical farming conditions, where environmental stability enabled the reliable detection of plant responses to contrast light spectra. Crop performance was evaluated through biomass accumulation, morphological traits, and nutritional quality parameters. The intelligent control system maintained environmental setpoints within narrow ranges throughout the cultivation cycle, minimizing microclimatic variability across vertical tiers. As a result, observed differences in plant growth and biochemical composition were less likely to be confounded by environmental drift. By shifting the role of IoT technologies from simple automation tools to experimental enablers, this work illustrates how intelligent climate control can support reproducibility, scalability, and methodological robustness in vertical horticulture research. The proposed open, modular architecture provides a transferable framework for reproducible crop experimentation and production in controlled-environment systems.